From 4ab3ea9799191e070e7cda450bd1d6bc2e35d582 Mon Sep 17 00:00:00 2001 From: Ashley Song <165685692+ashsong-nv@users.noreply.github.com> Date: Tue, 12 Nov 2024 13:27:23 -0500 Subject: [PATCH] Merge `ashsong_develop` branch into `main` (#3) This PR merges fixes and improvements from the `ashong_develop` branch into `main`. - Remove unused data files, including SBOMs, input messages, and outdated validation file - Add warning to quick start notebook about non-deterministic outputs - Remove unnecessary `debug_input` stage in `input.py` - Major improvements to `README` to address developer review feedback --- README.md | 199 +++--- configs/from_file.json | 2 +- .../rapidsai:23.08-cuda11.8-py3.10.json | 282 -------- .../rapidsai:24.06-cuda12.0-py3.9.json | 247 ------- .../morpheus/morpheus:23.07-runtime.sbom | 494 -------------- .../morpheus/morpheus:23.11-runtime.sbom | 465 ------------- .../morpheus/morpheus:24.03-runtime.sbom | 493 -------------- .../morpheus/morpheus:v23.07.03-runtime.sbom | 502 -------------- .../rapidsai/base:23.08-cuda11.8-py3.10.sbom | 621 ------------------ .../rapidsai/base:24.06-cuda12.2-py3.10.sbom | 599 ----------------- .../cve_validation_formatted_openai.json | 147 ----- quick_start/quick_start_guide.ipynb | 17 +- src/cve/pipeline/input.py | 11 - 13 files changed, 126 insertions(+), 3953 deletions(-) delete mode 100644 data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json delete mode 100644 data/input_messages/rapidsai:24.06-cuda12.0-py3.9.json delete mode 100644 data/sboms/nvcr.io/nvidia/morpheus/morpheus:23.07-runtime.sbom delete mode 100644 data/sboms/nvcr.io/nvidia/morpheus/morpheus:23.11-runtime.sbom delete mode 100644 data/sboms/nvcr.io/nvidia/morpheus/morpheus:24.03-runtime.sbom delete mode 100644 data/sboms/nvcr.io/nvidia/morpheus/morpheus:v23.07.03-runtime.sbom delete mode 100644 data/sboms/nvcr.io/nvidia/rapidsai/base:23.08-cuda11.8-py3.10.sbom delete mode 100644 data/sboms/nvcr.io/nvidia/rapidsai/base:24.06-cuda12.2-py3.10.sbom delete mode 100644 data/validation_output/cve_validation_formatted_openai.json diff --git a/README.md b/README.md index 6231503..545fdd4 100644 --- a/README.md +++ b/README.md @@ -30,28 +30,29 @@ limitations under the License. - [Key components](#key-components) - [NIM microservices](#nim-microservices) - [Getting started](#getting-started) - - [Prerequisites](#prerequisites-1) - - [Install system requirements](#install-system-requirements) - - [Obtain API keys](#obtain-api-keys) - - [Set up the workflow repository](#set-up-the-workflow-repository) - - [Environment setup](#environment-setup) - - [Set up the environment file](#set-up-the-environment-file) - - [Build the Docker containers](#build-the-docker-containers) + - [Install system requirements](#install-system-requirements) + - [Obtain API keys](#obtain-api-keys) + - [Set up the workflow repository](#set-up-the-workflow-repository) + - [Set up the environment file](#set-up-the-environment-file) + - [Authenticate Docker with NGC](#authenticate-docker-with-ngc) + - [Build the Docker containers (optional)](#build-the-docker-containers-optional) + - [Start the Docker containers](#start-the-docker-containers) + - [Using NVIDIA-hosted NIMs](#using-nvidia-hosted-nims) + - [Using self-hosted NIMs](#using-self-hosted-nims) - [Running the workflow](#running-the-workflow) - - [Run the workflow using NVIDIA-hosted NIMs](#run-the-workflow-using-nvidia-hosted-nims) - - [Run the workflow using self-hosted NIMs](#run-the-workflow-using-self-hosted-nims) - - [Starting the workflow](#starting-the-workflow) - [From the quick start user guide notebook](#from-the-quick-start-user-guide-notebook) - [From the command line](#from-the-command-line) - - [Command line interface](#command-line-interface) - - [Command line interface (CLI)](#command-line-interface-cli) + - [Configuration files](#configuration-files) + - [Example command: `from_manual.json`](#example-command-from_manualjson) + - [Reviewing the output](#reviewing-the-output) + - [Example command: `from_http.json`](#example-command-from_httpjson) + - [Command line interface (CLI) reference](#command-line-interface-cli-reference) - [Overriding options on the command line](#overriding-options-on-the-command-line) - [Example 1: Override `max_retries`](#example-1-override-max_retries) - [Example 2: Override input type](#example-2-override-input-type) - [Example 3: Combine multiple overrides](#example-3-combine-multiple-overrides) - [Full list of options](#full-list-of-options) - - [Configuration files](#configuration-files) - - [Configuration file breakdown](#configuration-file-breakdown) + - [Configuration file reference](#configuration-file-reference) - [NGINX caching server](#nginx-caching-server) - [Customizing the Workflow](#customizing-the-workflow) - [Customizing the input](#customizing-the-input) @@ -99,13 +100,15 @@ This blueprint is for: - Details can be found in this later section: [Obtain API keys](#obtain-api-keys) ## Hardware requirements -The vulnerability analysis pipeline supports the following hardware: +Below are the hardware requirements for each component of the vulnerability analysis pipeline. -- **Pipeline operation**: 1x L40 GPU recommended -- **LLM NIM**: [Meta Llama 3 70B Instruct Support Matrix](https://docs.nvidia.com/nim/large-language-models/latest/support-matrix.html#meta-llama-3-70b-instruct) (Optional) +The overall hardware requirements depend on selected pipeline configuration. At a minimum, the hardware requirements for pipeline operation must be met. The LLM NIM and Embedding NIM hardware requirements only need to be met if self-hosting these components. See [Using self-hosted NIMs](#using-self-hosted-nims), [Customizing the LLM models](#customizing-the-llm-models) and [Customizing the embedding model](#customizing-the-embedding-model) sections for more information. + +- **(Required) Pipeline operation**: 1x L40 GPU or similar recommended +- **(Optional) LLM NIM**: [Meta Llama 3 70B Instruct Support Matrix](https://docs.nvidia.com/nim/large-language-models/latest/support-matrix.html#meta-llama-3-70b-instruct) - For improved paralleled performance, we recommend 8x or more H100s for LLM inference. - The pipeline can share the GPU with the LLM NIM, but it is recommended to have a separate GPU for the LLM NIM for optimal performance. -- **Embedding NIM**: [NV-EmbedQA-E5-v5 Support Matrix](https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html#nv-embedqa-e5-v5) (Optional) +- **(Optional) Embedding NIM**: [NV-EmbedQA-E5-v5 Support Matrix](https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html#nv-embedqa-e5-v5) - The pipeline can share the GPU with the Embedding NIM, but it is recommended to have a separate GPU for the Embedding NIM for optimal performance. @@ -141,21 +144,21 @@ The detailed architecture consists of the following components: - **Security scan result**: The workflow begins by inputting the identified CVEs from a container security scan as input. This can be generated from a container image scanner of your choosing such as [Anchore](https://anchore.com/container-vulnerability-scanning/). -- **PreProcessing**: All the below actions are encapsulated by multiple Morpheus preprocessing pipeline stages to prepare the data for use with the LLM engine. (See `src/cve/pipeline/input.py`.) +- **PreProcessing**: All the below actions are encapsulated by multiple Morpheus preprocessing pipeline stages to prepare the data for use with the LLM engine. (See [`src/cve/pipeline/input.py`](./src/cve/pipeline/input.py).) - **Code repository and documentation**: The blueprint pulls code repositories and documentation provided by the user. These repositories are processed through an embedding model, and the resulting embeddings are stored in vector databases (VDBs) for the agent's reference. - **Vector database**: Various vector databases can be used for the embedding. We currently utilize FAISS for the VDB because it does not require an external service and is simple to use. Any vector store can be used, such as NVIDIA cuVS, which would provide accelerated indexing and search. - **Lexical search**: As an alternative, a lexical search is available for use cases where creating an embedding is impractical due to a large number of source files in the target container. - **Software Bill of Materials (SBOM)**: The provided SBOM document is processed into a software-ingestible format for the agent's reference. SBOMs can be generated for any container using the open-source tool [Syft](https://github.com/anchore/syft). - **Web vulnerability intel**: The system collects detailed information about each CVE through web scraping and data retrieval from various public security databases, including GHSA, Redhat, Ubuntu, and NIST CVE records, as well as tailored threat intelligence feeds. -- **Core LLM engine**: (See `src/cve/pipeline/engine.py`.) - - **Checklist generation**: Leveraging the gathered information about each vulnerability, the checklist generation node creates a tailored, context-sensitive task checklist designed to guide the impact analysis. (See `src/cve/nodes/cve_checklist_node.py`.) +- **Core LLM engine**: (See [`src/cve/pipeline/engine.py`](./src/cve/pipeline/engine.py).) + - **Checklist generation**: Leveraging the gathered information about each vulnerability, the checklist generation node creates a tailored, context-sensitive task checklist designed to guide the impact analysis. (See [`src/cve/nodes/cve_checklist_node.py`](./src/cve/nodes/cve_checklist_node.py).) - - **Task agent**: At the core of the process is an LLM agent iterating through each item in the checklist. For each item, the agent answers the question using a set of tools which provide information about the target container. The tools tap into various data sources (web intel, vector DB, search etc.), retrieving relevant information to address each checklist item. The loop continues until the agent resolves each checklist item satisfactorily. (See `src/cve/nodes/cve_langchain_agent_node.py`.) + - **Task agent**: At the core of the process is an LLM agent iterating through each item in the checklist. For each item, the agent answers the question using a set of tools which provide information about the target container. The tools tap into various data sources (web intel, vector DB, search etc.), retrieving relevant information to address each checklist item. The loop continues until the agent resolves each checklist item satisfactorily. (See [`src/cve/nodes/cve_langchain_agent_node.py`](./src/cve/nodes/cve_langchain_agent_node.py).) - - **Summarization**: Once the agent has compiled findings for each checklist item, these results are condensed by the summarization node into a concise, human-readable paragraph. (See `src/cve/nodes/cve_summary_node.py`.) + - **Summarization**: Once the agent has compiled findings for each checklist item, these results are condensed by the summarization node into a concise, human-readable paragraph. (See [`src/cve/nodes/cve_summary_node.py`](./src/cve/nodes/cve_summary_node.py).) - - **Justification Assignment**: Given the summary, the justification status categorization node then assigns a resulting VEX (Vulnerability Exploitability eXchange) status to the CVE. We provided a set of predefined categories for the model to choose from. (See `src/cve/nodes/cve_justification_node.py`.) If the CVE is deemed exploitable, the reasoning category is "vulnerable." If it is not exploitable, there are 10 different reasoning categories to explain why the vulnerability is not exploitable in the given environment: + - **Justification Assignment**: Given the summary, the justification status categorization node then assigns a resulting VEX (Vulnerability Exploitability eXchange) status to the CVE. We provided a set of predefined categories for the model to choose from. (See [`src/cve/nodes/cve_justification_node.py`](./src/cve/nodes/cve_justification_node.py).) If the CVE is deemed exploitable, the reasoning category is "vulnerable." If it is not exploitable, there are 10 different reasoning categories to explain why the vulnerability is not exploitable in the given environment: - `false_positive` - `code_not_present` - `code_not_reachable` @@ -167,10 +170,10 @@ The detailed architecture consists of the following components: - `protected_by_perimeter` - `protected_by_mitigating_control` -- **Output**: At the end of the pipeline run, an output file including all the gathered and generated information is prepared for security analysts for a final review. (See `src/cve/pipeline/output.py`.) +- **Output**: At the end of the pipeline run, an output file including all the gathered and generated information is prepared for security analysts for a final review. (See [`src/cve/pipeline/output.py`](./src/cve/pipeline/output.py).) - > [!WARNING] - > All output should be vetted by a security analyst before being used in a cybersecurity application. +> [!WARNING] +> All output should be vetted by a security analyst before being used in a cybersecurity application. ### NIM microservices The Morpheus SDK can utilize various embedding model and LLM endpoints, and is optimized to use [NVIDIA NIM microservices](https://developer.nvidia.com/nim) (NIMs). NIMs are pre-built containers for the latest AI models that provide industry-standard APIs and optimized inference for the given model and hardware. Using NIMs enables easy deployment and scaling for self-hosted model inference. @@ -185,15 +188,13 @@ The current default embedding NIM model is `nv-embedqa-e5-v5`, which was selecte ## Getting started -### Prerequisites - -#### Install system requirements +### Install system requirements * [git](https://git-scm.com/) * [git-lfs](https://git-lfs.com/) * Since the workflow uses an [NVIDIA Morpheus](https://developer.nvidia.com/morpheus-cybersecurity) pipeline, the [Morpheus requirements](https://docs.nvidia.com/morpheus/getting_started.html#requirements) also need to be installed. -#### Obtain API keys +### Obtain API keys To run the pipeline you need to obtain API keys for the following APIs. These will be needed in a later step to [Set up the environment file](#set-up-the-environment-file). - **Required API Keys**: These APIs are required by the pipeline to retrieve vulnerability information from databases, perform online searches, and execute LLM queries. @@ -219,8 +220,7 @@ To run the pipeline you need to obtain API keys for the following APIs. These wi The workflow can be configured to use other LLM services as well, see the [Customizing the LLM models](#customizing-the-llm-models) section for more info. - -#### Set up the workflow repository +### Set up the workflow repository Clone the repository and set an environment variable for the path to the repository root. @@ -230,9 +230,7 @@ export REPO_ROOT=$(git rev-parse --show-toplevel) All commands are run from the repository root unless otherwise specified. -### Environment setup - -#### Set up the environment file +### Set up the environment file First we need to create an `.env` file in the `REPO_ROOT`, and add the API keys you created in the earlier [Obtain API keys](#obtain-api-keys) step. @@ -251,8 +249,20 @@ These variables need to be exported to the environment: export $(cat .env | xargs) ``` -#### Build the Docker containers -Next we need to build the necessary containers. +### Authenticate Docker with NGC + +In order to pull images required by the workflow from NGC, you must first authenticate Docker with NGC. You can use same the NVIDIA API Key obtained in the [Obtain API keys](#obtain-api-keys) section (saved as `NVIDIA_API_KEY` in the `.env` file). + +```bash +$ docker login nvcr.io +Username: $oauthtoken +Password: < Insert NVIDIA API Key here > +``` + +### Build the Docker containers (optional) +If no customizations were made to the source code, you can proceed to [Starting the Docker containers](#start-the-docker-containers), where the `docker compose up` step will automatically pull the pre-built Blueprint Docker container from NGC: `nvcr.io/nvidia/morpheus/morpheus-vuln-analysis:24.10`. + +If any customizations are made to the source code, we will need to build the container from source using the following command: ```bash cd $REPO_ROOT @@ -260,12 +270,12 @@ cd $REPO_ROOT docker compose build morpheus-vuln-analysis ``` -## Running the workflow +### Start the Docker containers -To run the workflow, there are two supported configurations. Both configurations utilize `docker compose` to start the service: -1. [**NVIDIA-hosted NIMs**](#run-the-workflow-using-nvidia-hosted-nims): The workflow is run with all computation being performed by NIMs hosted in NVIDIA GPU Cloud. This is the default configuration and is recommended for most users getting started with the workflow. +There are two supported configurations for starting the Docker containers. Both configurations utilize `docker compose` to start the service: +1. [**NVIDIA-hosted NIMs**](#using-nvidia-hosted-nims): The workflow is run with all computation being performed by NIMs hosted in NVIDIA GPU Cloud. This is the default configuration and is **recommended** for most users getting started with the workflow. 1. When using NVIDIA-hosted NIMs, only the `docker-compose.yml` configuration file is required. -2. [**Self-hosted NIMs**](#run-the-workflow-using-self-hosted-nims): The workflow is run using self-hosted LLM NIM services. This configuration is more advanced and requires additional setup to run the NIM services locally. +2. [**Self-hosted NIMs**](#using-self-hosted-nims): The workflow is run using self-hosted LLM NIM services. This configuration is more advanced and requires additional setup to run the NIM services locally. 1. When using self-hosted NIMs, both the `docker-compose.yml` and `docker-compose.nim.yml` configuration files are required. These two configurations are illustrated by the following diagram: @@ -278,14 +288,14 @@ Before beginning, ensure that the environment variables are set correctly. Both >[!TIP] The container binds to port 8080 by default. If you encounter a port collision error (e.g. `Bind for 0.0.0.0:8080 failed: port is already allocated`), you can set the environment variable `NGINX_HOST_HTTP_PORT` to specify a custom port before launching `docker compose`. For example: +> +> ```bash +> export NGINX_HOST_HTTP_PORT=8081 +> +> #... docker compose commands... +> ``` -```bash -export NGINX_HOST_HTTP_PORT=8081 - -#... docker compose commands... -``` - -#### Run the workflow using NVIDIA-hosted NIMs +#### Using NVIDIA-hosted NIMs When running the workflow in this configuration, only the `morpheus-vuln-analysis` service needs to be started since we will utilize NIMs hosted by NVIDIA. The `morpheus-vuln-analysis` container can be started using the following command: ```bash @@ -298,9 +308,14 @@ The command above starts the container in the background using the detached mode docker compose ps ``` -Continue to the [Starting the workflow](#starting-the-workflow) section to run the workflow. +Next, we need to attach to the `morpheus-vuln-analysis` container to access the environment where the workflow [command line tool](#from-the-command-line) and dependencies are installed. +```bash +docker compose exec -it morpheus-vuln-analysis bash +``` -#### Run the workflow using self-hosted NIMs +Continue to the [Running the workflow](#running-the-workflow) section to run the workflow. + +#### Using self-hosted NIMs To run the workflow using self-hosted NIMs, we use a second `docker compose` configuration file, `docker-compose.nim.yml`, which adds the self-hosted NIM services to the workflow. Utilizing a second configuration file allows for easy switching between the two configurations while keeping the base configuration file the same. >[!NOTE] @@ -318,11 +333,16 @@ cd ${REPO_ROOT} docker compose -f docker-compose.yml -f docker-compose.nim.yml up -d ``` -Continue to the [Starting the workflow](#starting-the-workflow) section to run the workflow. +Next, we need to attach to the `morpheus-vuln-analysis` container to access the environment where the workflow [command line tool](#from-the-command-line) and dependencies are installed. +```bash +docker compose -f docker-compose.yml -f docker-compose.nim.yml exec -it morpheus-vuln-analysis bash +``` + +Continue to the [Running the workflow](#running-the-workflow) section to run the workflow. -### Starting the workflow +## Running the workflow -Once the services have been started, the workflow can be run using an [interactive notebook](#from-the-quick-start-user-guide-notebook) (for a guided, step-by-step process) or from the [command line](#from-the-command-line). +Once the services have been started, the workflow can be run using either the [Quick start user guide notebook](#from-the-quick-start-user-guide-notebook) for an interactive step-by-step process, or directly from the [command line](#from-the-command-line). ### From the quick start user guide notebook To run the workflow in an interactive notebook, connect to the Jupyter notebook at http://localhost:8888/lab. Once connected, navigate to the notebook located at [`quick_start/quick_start_guide.ipynb`](./quick_start/quick_start_guide.ipynb) and follow the instructions. @@ -336,34 +356,34 @@ To run the workflow in an interactive notebook, connect to the Jupyter notebook ### From the command line -To run the workflow from the command line, you can use the provided CLI tool. In order to use the CLI tool, we must first attach to the `morpheus-vuln-analysis` container: +The vulnerability analysis workflow is designed to be run using the command line tool installed within the `morpheus-vuln-analysis` container. This section describes how get started using the command line tool. For more detailed information about the command line interface, see the [Command line interface (CLI) reference](#command-line-interface-cli-reference) section. -```bash -docker compose exec -it morpheus-vuln-analysis bash -``` +#### Configuration files -If you are using self-hosted NIMs, make sure to use both configuration files: -```bash -docker compose -f docker-compose.yml -f docker-compose.nim.yml exec -it morpheus-vuln-analysis bash -``` +The pipeline settings are controlled using configuration files. These are JSON files that define various pipeline settings, such as the input data, the LLM models used, and the output format. Several example configuration files are located in the [`configs/`](./configs/) folder. A brief description of each configuration file is as follows: +- `from_manual.json`: This configuration file starts the pipeline using manually provided data. All pipeline inputs are manually specified directly in the configuration file. +- `from_file.json`: This configuration file starts the pipeline using data fetched from a file. The pipeline fetches the input data from a file and processes it. This is very similar to `from_manual.json`, but the input data is read from a file instead of being directly specified in the config file. +- `from_http.json`: This configuration file starts an HTTP server to turn the workflow into a microservice. The pipeline fetches the input data from an HTTP source and processes it. To trigger the pipeline, you can send a POST request to the `/scan` endpoint with the input data in the request body. The pipeline will process the input data. + +There are two main modalities that the pipeline can be run in. When using the `from_file.json` or `from_manual.json` configuration files, the pipeline will process the input data, then it will shut down after it is completed. This modality is suitable for rapid iteration during testing and development. When using `from_http.json`, the pipeline is turned into a microservice which will run indefinitely, which is suitable for using in production. -From here, you can run the workflow using the provided CLI tool outlined in the [following section](#command-line-interface). +For a breakdown of the configuration file and available options, see the [Configuration file reference](#configuration-file-reference) section. To customize the configuration files for your use case, see [Customizing the workflow](#customizing-the-workflow). -### Command line interface +#### Example command: `from_manual.json` -The pipeline can be started using the following command: +The workflow pipeline can be started using the following command: ```bash python src/main.py --log_level DEBUG \ cve pipeline --config_file=${CONFIG_FILE} ``` -Where `${CONFIG_FILE}` is the path to the configuration file you want to use. For example, to run the pipeline with the `from_manual.json` configuration file, you would run: +In the command, `${CONFIG_FILE}` is the path to the configuration file you want to use. For example, to run the pipeline with the `from_manual.json` configuration file, you would run: ```bash python src/main.py --log_level DEBUG \ cve pipeline --config_file=configs/from_manual.json ``` -This pipeline will run to completion and the output is stashed by default at `.tmp/output.json`. Sample logs are as follows: +When the pipeline runs to completion, you should see logs similar to the following: ``` Message elapsed time: 28.240849 sec Vulnerability 'GHSA-3f63-hfp8-52jq' affected status: FALSE. Label: code_not_reachable @@ -379,14 +399,26 @@ LLM[Complete]: 7 messages [00:42, 6.05s/ messages] ====Pipeline Complete==== Total time: 45.61 sec Pipeline runtime: 42.69 sec - ``` -In addition to the raw JSON output in `.tmp/output.json`, you can also view a Markdown-formatted report for each CVE in the `.tmp/vulnerability_markdown_reports directory`. - > [!WARNING] > The output you receive from the pipeline may not be identical as the output in the example above. The output may vary due to the non-deterministic nature of the LLM models. +##### Reviewing the output + +The full pipeline JSON output is stashed by default at `.tmp/output.json`. The output JSON includes the following top level fields: + +* `input`: contains the inputs that were provided to the pipeline, such as the container and repo source information, the list of vulnerabilities to scan, etc. +* `info`: contains additional information collected by the pipeline for decision making. This includes paths to the generated VDB files, intelligence from various vulnerability databases, the list of SBOM packages, and any vulnerable dependencies that were identified. +* `output`: contains the output from the core LLM Engine, including the generated checklist, analysis summary, and justification assignment. + +In addition to the raw JSON output, you can also view a Markdown-formatted report for each CVE in the `.tmp/vulnerability_markdown_reports` directory. This view is helpful for human analysts reviewing the results. + +> [!TIP] +> To return detailed steps taken by the LLM agent in the output, set `return_intermediate_steps` to `true` in the configuration file. This can be helpful for explaining the output, and for troubleshooting unexpected results. + +#### Example command: `from_http.json` + Similarly, to run the pipeline with the `from_http.json` configuration file, you would run: ```bash python src/main.py --log_level DEBUG cve pipeline --config_file=configs/from_http.json @@ -407,9 +439,9 @@ In this command: Note that the results of the pipeline are not returned to the curl request. After processing the request, the server will save the results to the output path specified in the configuration file. The server will also display log and summary results from the workflow as it's running. Additional submissions to the server will append the results to the specified output file. -### Command line interface (CLI) +### Command line interface (CLI) reference -The top level entrypoint to each of the LLM example pipelines is `src/main.py`. The main entrypoint is a CLI tool with a built documentation using the `--help` command. For example, to see what commands are available, you can run: +The top level entrypoint to each of the LLM example pipelines is `src/main.py`. The main entrypoint is a CLI tool with built-in documentation using the `--help` command. For example, to see what commands are available, you can run: ``` (morpheus) root@58145366033a:/workspace_examples# python src/main.py --help Usage: morpheus_llm [OPTIONS] COMMAND [ARGS]... @@ -451,31 +483,24 @@ It's also possible to change the input type. For example, to use a different inp python src/main.py --log_level=DEBUG \ cve pipeline --config_file=configs/from_file.json \ config \ - input-file --file=data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json + input-file --file=data/input_messages/morpheus:24.03-runtime.json ``` ##### Example 3: Combine multiple overrides -It's possible to combine multiple overrides in a single command. For example, to run the pipeline with the `from_manual.json` configuration file, but with the `max_retries` option set to `3`, the input message set to `rapidsai:23.08-cuda11.8-py3.10.json`, and the output destination set to `output_rapids.json` you can run: +It's possible to combine multiple overrides in a single command. For example, to run the pipeline with the `from_manual.json` configuration file, but with the `max_retries` option set to `3`, the input message set to `morpheus:24.03-runtime.json`, and the output destinations set to `.tmp/output_morpheus.json` and `.tmp/morpheus_reports`, you can run: ```bash python src/main.py --log_level=DEBUG \ cve pipeline --config_file=configs/from_manual.json \ config \ general --max_retries=3 \ - input-file --file=data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json - output-file --file_path=output_rapids.json --markdown_dir=rapids_reports + input-file --file=data/input_messages/morpheus:24.03-runtime.json \ + output-file --file_path=.tmp/output_morpheus.json --markdown_dir=.tmp/morpheus_reports ``` #### Full list of options For the full list of possible options, use the `--help` options from the CLI. -### Configuration files - -The pipeline settings are controlled using configuration files. These files are JSON files that define the pipeline settings, such as the input data, the LLM models used, and the output format. Several example configuration files are located in the `configs/` folder. A brief description of each configuration file is as follows: -- `from_manual.json`: This configuration file is used to demonstrate the pipeline using manually provided data. All pipeline inputs are manually specified directly in the configuration file. -- `from_file.json`: This configuration file is used to demonstrate the pipeline using data fetched from a file. The pipeline fetches the input data from a file and processes it. Very similar to `from_manual.json`, but the input data is read from a file instead of being directly specified in the config file. -- `from_http.json`: This configuration file starts an HTTP server to turn the workflow into a microservice. The pipeline fetches the input data from an HTTP source and processes it. To trigger the pipeline, you can send a POST request to the `/scan` endpoint with the input data in the request body. The pipeline will process the input data. - -#### Configuration file breakdown +### Configuration file reference The configuration defines how the workflow operates, including model settings, input sources, and output options. 1. **Schema** @@ -578,7 +603,7 @@ To use a file as the input source, update the JSON object in the config file to: ``` "input": { "_type": "file", - "file": "data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json" + "file": "data/input_messages/morpheus:23.11-runtime.json" } ``` @@ -764,7 +789,7 @@ When building containers for self-hosted NIMs, certain issues may occur. Below a #### Device error -If you encounter an error resembling the following during the [container build process for self-hosted NIMs](#run-the-workflow-using-self-hosted-nims): +If you encounter an error resembling the following during the [container build process for self-hosted NIMs](#using-self-hosted-nims): ```bash nvidia-container-cli: device error: {n}: unknown device: unknown @@ -778,7 +803,7 @@ This adjustment ensures the container accurately matches the available GPU resou #### Deploy.Resources.Reservations.devices error -If you encounter an error resembling the following during the [container build process for self-hosted NIMs](#run-the-workflow-using-self-hosted-nims) process: +If you encounter an error resembling the following during the [container build process for self-hosted NIMs](#using-self-hosted-nims) process: ``` 1 error(s) decoding: diff --git a/configs/from_file.json b/configs/from_file.json index 21fc9f9..2e74f8c 100644 --- a/configs/from_file.json +++ b/configs/from_file.json @@ -62,7 +62,7 @@ }, "input": { "_type": "file", - "file": "data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json" + "file": "data/input_messages/morpheus:23.11-runtime.json" }, "output": { "_type": "file", diff --git a/data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json b/data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json deleted file mode 100644 index 60a83d5..0000000 --- a/data/input_messages/rapidsai:23.08-cuda11.8-py3.10.json +++ /dev/null @@ -1,282 +0,0 @@ -{ - "image": { - "name": "nvcr.io/nvidia/rapidsai/base", - "tag": "24.06-cuda12.2-py3.10", - "source_info": [ - // cuDF - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cudf.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cudf.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuML - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuml.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuml.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuGraph - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cugraph.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cugraph.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // RMM - { - "type": "code", - "git_repo": "https://github.com/rapidsai/RMM.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/RMM.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // RAFT - { - "type": "code", - "git_repo": "https://github.com/rapidsai/RAFT.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/RAFT.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuSpatial - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuSpatial.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuSpatial.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuxfilter - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuxfilter.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuxfilter.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuCIM - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuCIM.git", - "tag": "v23.08.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuCIM.git", - "tag": "v23.08.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - } - ], - "sbom_info": { - "_type": "file", - "file_path": "data/sboms/nvcr.io/nvidia/rapidsai/base:23.08-cuda11.8-py3.10.sbom" - } - }, - "scan": { - "vulns": [ - { - "vuln_id": "GHSA-5wvp-7f3h-6wmm" - }, - { - "vuln_id": "GHSA-w3vc-fx9p-wp4v" - }, - { - "vuln_id": "CVE-2024-0985" - }, - { - "vuln_id": "GHSA-94vc-p8w7-5p49" - }, - { - "vuln_id": "GHSA-q3qx-c6g2-7pw2" - }, - { - "vuln_id": "CVE-2023-5869" - }, - { - "vuln_id": "GHSA-2jv5-9r88-3w3p" - }, - { - "vuln_id": "CVE-2023-5363" - }, - { - "vuln_id": "GHSA-2jv5-9r88-3w3p" - }, - { - "vuln_id": "GHSA-3f63-hfp8-52jq" - }, - { - "vuln_id": "CVE-2023-4911" - }, - { - "vuln_id": "GHSA-56pw-mpj4-fxww" - }, - { - "vuln_id": "CVE-2023-4911" - }, - { - "vuln_id": "GHSA-6673-4983-2vx5" - }, - { - "vuln_id": "CVE-2023-4911" - }, - { - "vuln_id": "CVE-2023-4807" - }, - { - "vuln_id": "CVE-2023-36632" - }, - { - "vuln_id": "GHSA-6vqw-3v5j-54x4" - }, - { - "vuln_id": "GHSA-j7hp-h8jx-5ppr" - }, - { - "vuln_id": "CVE-2023-36632" - }, - { - "vuln_id": "GHSA-5m98-qgg9-wh84" - }, - { - "vuln_id": "GHSA-3ww4-gg4f-jr7f" - } - ] - } -} diff --git a/data/input_messages/rapidsai:24.06-cuda12.0-py3.9.json b/data/input_messages/rapidsai:24.06-cuda12.0-py3.9.json deleted file mode 100644 index 5bc7528..0000000 --- a/data/input_messages/rapidsai:24.06-cuda12.0-py3.9.json +++ /dev/null @@ -1,247 +0,0 @@ -{ - "image": { - "name": "nvcr.io/nvidia/rapidsai/base", - "tag": "24.06-cuda12.2-py3.10", - "source_info": [ - // cuDF - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cudf.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cudf.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuML - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuml.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuml.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuGraph - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cugraph.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cugraph.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuVS - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuvs.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuvs.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // RMM - { - "type": "code", - "git_repo": "https://github.com/rapidsai/RMM.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/RMM.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // RAFT - { - "type": "code", - "git_repo": "https://github.com/rapidsai/RAFT.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/RAFT.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuSpatial - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuSpatial.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuSpatial.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuxfilter - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuxfilter.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuxfilter.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - }, - // cuCIM - { - "type": "code", - "git_repo": "https://github.com/rapidsai/cuCIM.git", - "tag": "v24.06.00", - "include": [ - "**/*.cpp", - "**/*.cu", - "**/*.cuh", - "**/*.h", - "**/*.hpp", - "**/*.ipynb", - "**/*.py", - "**/*Dockerfile" - ] - }, - { - "type": "doc", - "git_repo": "https://github.com/rapidsai/cuCIM.git", - "tag": "v24.06.00", - "include": [ - "**/*.md", - "docs/**/*.rst" - ] - } - ], - "sbom_info": { - "_type": "file", - "file_path": "data/sboms/nvcr.io/nvidia/rapidsai/base:24.06-cuda12.2-py3.10.sbom" - } - }, - "scan": { - "vulns": [ - { - "vuln_id": "CVE-2023-36632" - }, - { - "vuln_id": "GHSA-f5x3-32g6-xq36" - } - ] - } -} diff --git a/data/sboms/nvcr.io/nvidia/morpheus/morpheus:23.07-runtime.sbom b/data/sboms/nvcr.io/nvidia/morpheus/morpheus:23.07-runtime.sbom deleted file mode 100644 index 79026ec..0000000 --- a/data/sboms/nvcr.io/nvidia/morpheus/morpheus:23.07-runtime.sbom +++ /dev/null @@ -1,494 +0,0 @@ -NAME VERSION TYPE -@jupyter-widgets/jupyterlab-manager 5.0.8 npm [38;2;119;119;119m[0m -@jupyterlab/application-top 4.0.3 npm [38;2;119;119;119m(+2 duplicates)[0m -@jupyterlab/galata-extension 5.0.2 npm [38;2;119;119;119m[0m -@jupyterlab/mock-consumer 4.0.3 npm [38;2;119;119;119m[0m -@jupyterlab/mock-extension 4.0.3 npm [38;2;119;119;119m[0m -@jupyterlab/mock-incompat 0.1.0 npm [38;2;119;119;119m[0m -@jupyterlab/mock-mime-extension 0.3.0 npm [38;2;119;119;119m[0m -@jupyterlab/mock-package 0.1.0 npm [38;2;119;119;119m[0m -@jupyterlab/mock-provider 4.0.3 npm [38;2;119;119;119m[0m -@jupyterlab/mock-token 4.0.3 npm [38;2;119;119;119m[0m -Babel 2.12.1 python [38;2;119;119;119m[0m -Brotli 1.0.9 python [38;2;119;119;119m(+1 duplicate)[0m -ConfigArgParse 1.5.5 python [38;2;119;119;119m[0m -Flask 2.3.2 python [38;2;119;119;119m[0m -GitPython 3.1.32 python [38;2;119;119;119m[0m -Jinja2 3.1.2 python [38;2;119;119;119m(+1 duplicate)[0m -Mako 1.2.4 python [38;2;119;119;119m[0m -Markdown 3.4.4 python [38;2;119;119;119m[0m -MarkupSafe 2.1.3 python [38;2;119;119;119m(+1 duplicate)[0m -Pillow 10.0.0 python [38;2;119;119;119m[0m -PyJWT 2.8.0 python [38;2;119;119;119m[0m -PySocks 1.7.1 python [38;2;119;119;119m(+1 duplicate)[0m -PyYAML 6.0 python [38;2;119;119;119m(+1 duplicate)[0m -Pygments 2.15.1 python [38;2;119;119;119m(+1 duplicate)[0m -SQLAlchemy 2.0.0 python [38;2;119;119;119m[0m -Send2Trash 1.8.2 python [38;2;119;119;119m[0m -Simple Launcher 1.1.0.14 dotnet [38;2;119;119;119m(+5 duplicates)[0m -Sphinx 7.0.1 python [38;2;119;119;119m[0m -Werkzeug 2.3.6 python [38;2;119;119;119m[0m -absl-py 1.4.0 python [38;2;119;119;119m[0m -adduser 3.118ubuntu5 deb [38;2;119;119;119m[0m -alabaster 0.7.13 python [38;2;119;119;119m[0m -alembic 1.11.1 python [38;2;119;119;119m[0m -anyio 3.7.1 python [38;2;119;119;119m(+1 duplicate)[0m -appdirs 1.4.4 python [38;2;119;119;119m[0m -apt 2.4.9 deb [38;2;119;119;119m[0m -argon2-cffi 21.3.0 python [38;2;119;119;119m[0m -argon2-cffi-bindings 21.2.0 python [38;2;119;119;119m[0m -arrow 1.2.3 python [38;2;119;119;119m[0m -asn1crypto 1.5.1 python [38;2;119;119;119m[0m -asttokens 2.2.1 python [38;2;119;119;119m[0m -async-lru 2.0.4 python [38;2;119;119;119m[0m -attrs 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-zlib1g 1:1.2.11.dfsg-2ubuntu9.2 deb [38;2;119;119;119m[0m -zstandard 0.19.0 python [38;2;119;119;119m[0m diff --git a/data/validation_output/cve_validation_formatted_openai.json b/data/validation_output/cve_validation_formatted_openai.json deleted file mode 100644 index 7477efb..0000000 --- a/data/validation_output/cve_validation_formatted_openai.json +++ /dev/null @@ -1,147 +0,0 @@ -[ - { - "cve": "CVE-2018-21028", - "input_check_list": [ - "Check the Boa version: The vulnerability affects Boa versions up to and including 0.94.14rc21. Verify the version of Boa running in the Docker container. If it is using a vulnerable version, consider updating to a patched version.", - "Identify potential memory leaks: The vulnerability involves missing calls to the free function, leading to memory leaks. Review the code and configuration of the applications running in the Docker container to identify any instances where resources are not being released properly.", - "Assess the impact: The vulnerability does not have a direct impact on confidentiality or integrity (CVSS:C:N/I:N). However, it can lead to a denial of service (CVSS:A:H). Evaluate the potential impact of a denial of service attack on the Docker container and the applications it hosts.", - "Evaluate network exposure: Determine if the Docker container or any services within it are exposed to the network. Assess the potential for remote attackers to exploit the vulnerability by triggering memory leaks.", - "Consider the CWE description: The CWE description highlights the importance of releasing resources after their effective lifetime has ended. Review the code and configuration of the applications running in the Docker container to ensure that resources are properly released when no longer needed.", - "Verify the disputed status: Check if there are any disputes regarding the vulnerability. It is important to consider the credibility and consensus among security researchers and vendors regarding the existence and severity of the vulnerability." - ], - "output_check_list": [ - "I don't know the version of Boa running in the Docker container.", - "To prevent memory leaks in a Docker container, you can use the --scale option when running docker-compose up to limit the number of instances running. In practice, 3 instances have been shown to work well. Use as many instances as required. Keep in mind each instance takes about 1 Gb of memory.", - "Based on the information gathered, the potential impact of a denial of service attack on the Docker container and the applications it hosts can vary depending on the specific vulnerabilities present in the software versions used. It is important to further investigate if any vulnerable functions or components are used in the Docker container's code or its dependencies to fully assess the impact.", - "The Docker container is not exposed to the network, reducing the potential for remote attackers to exploit the vulnerability by triggering memory leaks. However, to fully assess its vulnerability, we need more information about the libraries and dependencies used by the Docker container. Unfortunately, I don't have access to the Docker Container Software Bill of Materials QA System to gather this information. We can try using the Internet Search tool to find relevant resources or consult with Docker documentation for more details.", - "Based on the information gathered, it is important to review the code and configuration of the applications running in the Docker container to ensure that resources are properly released when no longer needed. The Docker Container Code QA System did not provide any specific information about the applications using proper resource release mechanisms. The Docker Container Developer Guide QA System mentioned that the Docker container is responsible for managing resources such as memory, CPU, and disk space, but did not provide specific details about resource release. The Docker Container Software Bill of Materials QA System directed me to the Docker website, but an internet search did not provide specific information about the libraries used in the Docker container and their resource release mechanisms. Therefore, further investigation may be required to determine how resources are released in the Docker container.", - "The disputed status of a vulnerability can be determined by considering information from security researchers and vendors, as well as the reasons why a CVE ID may be rejected or disputed." - ] - }, - { - "cve": "CVE-2023-36632", - "input_check_list": [ - "Check the Python version: The vulnerability affects Python versions through 3.11.4. If your Docker container is running Python, verify the version. If it is through 3.11.4, it may be vulnerable.", - "Identify usage of the legacy email.utils.parseaddr function: Check all applications within your Docker container for usage of the legacy email.utils.parseaddr function. This function is categorized as a Legacy API, and applications are recommended to use the email.parser.BytesParser or email.parser.Parser class instead. If any application is using the legacy function, it may be vulnerable.", - "Evaluate the input data: Determine if the applications input data includes a name and an email address. If so, consider whether the input data is untrusted and potentially crafted by an attacker. If untrusted input data is passed to the legacy email.utils.parseaddr function, it may trigger the vulnerability.", - "Assess resource consumption: The vulnerability can lead to a \"RecursionError: maximum recursion depth exceeded while calling a Python object.\" Consider whether the Docker container has resource limits in place to prevent excessive recursion and control resource consumption. If not, it may be more susceptible to this vulnerability.", - "Vendor perspective: Note that the vendors perspective is that this is neither a vulnerability nor a bug. However, it is still important to assess the impact and potential risks within your specific environment.", - "Network exposure: Determine if the Docker container has network exposure, especially if the vulnerable Python code or any service using it is exposed to the internet. This will help assess the potential attack surface.", - "CVSS score: The CVSS 3.1 score for this vulnerability is 4.13, indicating a moderate severity. Consider this score in relation to your organization's risk tolerance and prioritize accordingly." - ], - "output_check_list": [ - "The Python version in the Docker container is 3.10.6.", - "The legacy email.utils.parseaddr function is not used in any of the applications within the Docker container, so it is not vulnerable to this specific legacy API.", - "The input data in the Docker container does not include a name and an email address, and therefore does not trigger the vulnerability.", - "No, the Docker container does not have resource limits in place.", - "The Docker container includes the following libraries in its software bill of materials: libassuan0, libatomic1, libattr1, libbsd0, libbz2-1.0, libc-bin, libisl23, libitm1, libjq1, NETStandard.Library, and Newtonsoft.Json. The Docker container's software version is within the vulnerability range of 1.0.0 to 1.2.0. I have searched the CVE system and found information about the reported CVE.", - "To determine if the Docker container has network exposure, it is necessary to analyze the Docker API to check for network-related functions or components used within the container. This can provide information about the network configuration and help assess the potential attack surface.", - "The reported CVE has a CVSS score of 4.13, indicating a moderate severity. The Docker container uses the following libraries and components: libassuan0, libatomic1, libattr1, docker/cli-docs-tool, docker/cli/cmd/docker, docker/compose-switch, docker/compose/v2, docker/distribution, and docker/docker. The reported vulnerability may affect the Docker container, and further investigation is needed to gather more information about the specific CVE." - ] - }, - { - "cve": "CVE-2023-4033", - "input_check_list": [ - "Check the mlflow/mlflow version: The vulnerability affects mlflow/mlflow versions prior to 2.6.0. If your Docker container is running mlflow/mlflow, verify the version. If its prior to 2.6.0, its vulnerable and you should update to version 2.6.0 or later.", - "Evaluate the OS command injection risk: Since the vulnerability involves OS command injection, you should consider whether your applications are constructing OS commands using externally-influenced input. Check if any applications within your Docker container are accepting user-supplied inputs to construct OS commands.", - "Assess the impact: The vulnerability has a CVSS 3.1 score of 4.13, indicating a high severity. Consider the potential impact of an attacker executing unexpected, dangerous commands directly on the operating system. Assess the privileges of the compromised process and the potential damage it can cause.", - "Network exposure: Determine if your Docker container has network exposure, especially if the mlflow/mlflow service or any service using it is exposed to the internet. This will help assess the attack surface and potential entry points for exploitation.", - "Mitigation: If your Docker container is vulnerable, consider implementing mitigations such as input validation and sanitization to prevent OS command injection attacks. Additionally, ensure that the principle of least privilege is followed to limit the privileges of the compromised process.", - "Note: Its important to stay updated with the latest security advisories and patches from the mlflow/mlflow project to address any vulnerabilities promptly." - ], - "output_check_list": [ - "Agent stopped due to iteration limit or time limit.", - "The Docker container does not have any applications that accept user-supplied inputs to construct OS commands, so the OS command injection risk is low.", - "The privileges of the compromised process in the Docker container can be determined by using the docker inspect command with the appropriate container ID.", - "The mlflow/mlflow service or any service using it in the Docker container is likely exposed to the internet through port 5000, indicating potential network exposure and entry points for exploitation. Further investigation into the network configuration, settings, and specific libraries and dependencies used is necessary to fully assess the impact of the reported CVE on the Docker container.", - "Some mitigation strategies for a vulnerable Docker container include using multi-stage builds, rebuilding images, and checking the image for vulnerabilities.", - "The Docker container is not impacted by the reported CVE." - ] - }, - { - "cve": "CVE-2022-32212", - "input_check_list": [ - "Check the Node.js version: The vulnerability affects Node.js versions less than 14.20.0, less than 16.20.0, and less than 18.5.0. If your Docker container is running Node.js, verify the version. If it's below the mentioned versions, it's vulnerable, and you should update to a secure version.", - "Evaluate the command injection risk: Since the vulnerability involves OS command injection, you should consider whether your applications are constructing OS commands using externally-influenced input. Check if your applications accept user input that is used to construct OS commands. If so, this is a potential attack vector.", - "Assess the IsAllowedHost check: The vulnerability is due to an insufficient IsAllowedHost check that can be bypassed. Review your code to determine if it uses the IsAllowedHost check and if it properly validates IP addresses before making DBS requests. If the check is not implemented correctly, it could allow for rebinding attacks.", - "Network exposure: Consider whether your Docker container has network exposure, especially if the Node.js service or any service using it is exposed to the internet. This vulnerability has a network attack vector, so it's important to assess the potential impact.", - "Verify the CVSS score: The CVSS 3.1 score for this vulnerability is 4.13, indicating a high severity. Take this into account when prioritizing your remediation efforts.", - "Check for vendor patches: Check if the Node.js project has released any patches or updates to address this vulnerability. If patches are available, apply them to your Docker container.", - "Monitor for security advisories: Stay updated on any security advisories related to this vulnerability. Subscribe to relevant mailing lists or follow trusted sources to receive timely information about any new developments or mitigations.", - "Remember that this checklist is preliminary, and it's important to conduct a thorough assessment of your specific environment and applications to ensure accurate vulnerability management." - ], - "output_check_list": [ - "The Docker container does not use Node.js, so it is not vulnerable to the reported CVE.", - "There is no command injection risk in the Docker container.", - "The Docker container's code does not implement the IsAllowedHost check correctly, which could potentially allow for rebinding attacks.", - "Based on the information gathered, the reported CVE does not have a direct impact on the Docker container. The Docker container has network exposure, but the Node.js service or any service using it is not exposed to the internet. Additionally, none of the libraries used by the Docker container contain the vulnerable function or component related to the reported CVE. Therefore, the potential impact of the vulnerability on the Docker container is limited. However, it is still important to stay updated on any new information or patches related to the reported CVE.", - "Agent stopped due to iteration limit or time limit.", - "There are no vendor patches or updates available for the Node.js project that can be applied to the Docker container, as Node.js does not appear to be installed or used in the container.", - "The security advisory for the reported CVE on the Docker container mentions a vulnerability in Docker versions before 9.03.15 and 20.10.3, where pulling an intentionally malformed Docker image manifest can cause a crash.", - "None" - ] - }, - { - "cve": "CVE-2022-45142", - "input_check_list": [ - "Check the Heimdal version: The vulnerability affects Heimdal versions 7.7.1 and 7.8.0. If your Docker container is running Heimdal, verify the version. If it matches one of the vulnerable versions, it may be vulnerable.", - "Check for any software using vulnerable Heimdal versions: Other applications might use Heimdal and be vulnerable. Check all applications within your Docker container for dependencies on vulnerable versions of Heimdal.", - "Evaluate the impact of the vulnerability: The vulnerability affects the validation of message integrity codes in gssapi/arcfour. Consider whether your applications are using gssapi/arcfour and if the integrity of the messages is critical. If so, this vulnerability may pose a risk.", - "Check for the fix: The fix for CVE-2022-3437 included changing memcmp to be constant time and a workaround for a compiler bug. Verify if these patches were backported to the Heimdal version in your Docker container. If the fix is present, the vulnerability may be mitigated.", - "Assess the CVSS score: The CVSS score for this vulnerability is 3.1, indicating a moderate severity. Consider the impact and exploitability of the vulnerability based on this score.", - "Network exposure: Determine if your Docker container has network exposure, especially if the Heimdal service or any service using it is exposed to the internet. This will help assess the potential risk of exploitation.", - "Verify the CWE description: The vulnerability is related to improper validation of integrity check values. Review the CWE description and consider if your applications are susceptible to this type of vulnerability." - ], - "output_check_list": [ - "The Docker container is not vulnerable to the reported CVE because it does not use Heimdal.", - "I was unable to determine if any application within the Docker container uses vulnerable versions of Heimdal. However, you can find the Docker container's software bill of materials (SBOM) on the github.com/docker/cli-docs-tool page. Additionally, you can use the Internet Search to find information about vulnerable versions of the Heimdal library.", - "The Docker container under investigation does not use gssapi/arcfour, so the reported vulnerability does not pose a risk to the integrity of the messages.", - "Agent stopped due to iteration limit or time limit.", - "To assess the impact and exploitability of the vulnerability with a CVSS score of 3.1, I will use the CVSS calculator for version 3.1 to input the specific metrics and calculate its impact and exploitability.", - "Based on the information gathered, the purpose and functionality of the Docker container do not directly indicate network exposure or mention the Heimdal service. However, further investigation is needed to assess the potential risk of network exposure and any vulnerabilities associated with the Heimdal service.", - "The CWE description for improper validation of integrity check values is related to the product not validating or incorrectly validating the integrity check values or \"checksums\" of a message. This may prevent it from detecting if the data has been modified or corrupted in transmission." - ] - }, - { - "cve": "CVE-2021-33910", - "input_check_list": [ - "Check the version of systemd: The vulnerability affects systemd versions prior to 246.15, 247.8, 248.5, and 249.1. Verify the version of systemd running in the Docker container. If it is a vulnerable version, further investigation is required.", - "Evaluate the impact of the vulnerability: The vulnerability results in an operating system crash, indicating a potential denial of service (DoS) attack. Consider the potential impact of a DoS attack on the Docker container and the services running within it.", - "Assess the attack vector: The vulnerability has a local attack vector, meaning that an attacker needs local access to the system to exploit it. Determine if the Docker container is exposed to potential attackers with local access.", - "Review resource allocation practices: The vulnerability is related to the allocation of resources without limits or throttling. Assess the resource allocation practices within the Docker container and check if there are any instances where resources are allocated without appropriate restrictions.", - "Consider the severity: The CVSS 3.1 score for this vulnerability is 7.5, indicating a high severity. Take into account the severity level when prioritizing the vulnerability assessment and mitigation efforts.", - "Check for available patches or updates: Check if there are any patches or updates available for the vulnerable version of systemd. If patches are available, apply them to mitigate the vulnerability.", - "Monitor for any signs of exploitation: Keep an eye on system logs and monitor for any signs of exploitation or abnormal behavior that could indicate an attempted or successful attack.", - "Note: This checklist provides a starting point for assessing the vulnerability in a Docker container. It is important to conduct a thorough analysis and consider other factors specific to the environment and deployment to ensure accurate vulnerability assessment and mitigation." - ], - "output_check_list": [ - "The version of systemd running in the Docker container is not vulnerable.", - "A denial of service (DoS) attack on a Docker container can potentially disrupt the availability of the container and the services running within it, leading to service downtime and loss of productivity. The impact of the attack depends on the criticality of the services or applications hosted by the Docker container.", - "Based on the information gathered, if the Docker container is running an affected version of Docker Desktop (before 4.23.0), it may be exposed to potential attackers with local access.", - "Based on the information from the Docker Container Developer Guide QA System, it appears that the Docker container has resource allocation practices in place that require 1 Gb of memory for each instance.", - "Based on the available information, I cannot provide a definitive answer about the impact of the reported CVE on the Docker container. Further investigation is needed, including obtaining the Docker container's software bill of materials (SBOM) and analyzing its dependencies. Additionally, more specific information about the reported CVE and its impact on Docker containers may need to be obtained through other means.", - "There are no patches or updates available for the vulnerable version of systemd in the Docker container.", - "To monitor for signs of exploitation or abnormal behavior in a Docker container, you can use the output from detected anomalies published to an S3 bucket, directory, or Kafka topic and integrate it with a monitoring tool.", - "No action is required as this is not a question." - ] - }, - { - "cve": "CVE-2015-20107", - "input_check_list": [ - "Check the Python version: The vulnerability affects Python versions up to 3.10.8, as well as versions 3.7, 3.8, and 3.9. If your Docker container is running Python, verify the version. If its within the affected range, its vulnerable and you should update to a patched version.", - "Identify applications using Python: Determine if any applications within your Docker container are using Python. Check for dependencies on vulnerable versions of Python. If any applications are using the affected versions, they may be vulnerable.", - "Evaluate the usage of the mailcap module: Since the vulnerability involves the mailcap module, check if any applications within your Docker container are using this module. Consider whether these applications are using untrusted input as part of commands executed through the mailcap module. If so, this is a potential attack vector.", - "Assess the risk of command injection: Given that the vulnerability allows for command injection, evaluate whether the applications within your Docker container are susceptible to this type of attack. Consider if the applications accept untrusted input that is used to construct commands executed by the application. If so, this increases the risk of exploitation.", - "Network exposure: Determine if your Docker container has network exposure, especially if the Python service or any service using it is exposed to the internet. This will help assess the potential impact of an attack exploiting the vulnerability.", - "Check for patched versions: Verify if patched versions of Python are available for your Docker container. If patches are available, update to the latest version to mitigate the vulnerability." - ], - "output_check_list": [ - "Agent stopped due to iteration limit or time limit.", - "The Docker container does not provide any information about the applications within it or the libraries it contains. However, the Docker container is used to set up dependencies and ensure a consistent environment for the workflow. It can be built and run using specific commands. To investigate if any applications within the Docker container are using Python and if they are using vulnerable versions, further analysis is required.", - "Agent stopped due to iteration limit or time limit.", - "The risk of command injection in the Docker container is low.", - "Based on the information available, it is not possible to determine if the Docker container has network exposure.", - "I don't know the version of the Python library in the Docker container." - ] - } -] \ No newline at end of file diff --git a/quick_start/quick_start_guide.ipynb b/quick_start/quick_start_guide.ipynb index 3298d2a..d0cf9e8 100644 --- a/quick_start/quick_start_guide.ipynb +++ b/quick_start/quick_start_guide.ipynb @@ -10,9 +10,10 @@ "For full instructions on running and customizing the blueprint, please see the [README](../README.md).\n", "\n", "## Prerequisites\n", - "* [Build and launch Morpheus Vulnerability Analysis container](../README.md#prerequisites)\n", + "* [Build and launch Morpheus Vulnerability Analysis container](../README.md#getting-started)\n", " + Be sure to keep the container bash shell handy for launching the HTTP server\n", "* From the host machine (outside the container), open this notebook in a [Jupyter server](https://docs.jupyter.org/en/latest/running.html), or from an IDE that supports notebook execution, such as [VS Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks).\n", + " + A running Jupyter server is included with the container by default. Instructions to connect can be found in this README section: [From the quick start user guide notebook](../README.md#from-the-quick-start-user-guide-notebook).\n", " + Please keep this notebook in its original directory, and ensure the repository structure has not been modified before running the notebook.\n", "* [Install `pandas`](https://pandas.pydata.org/docs/getting_started/install.html)\n", "* [Install Syft](https://github.com/anchore/syft) (optional prerequisite for cell 4, Generating a custom SBOM)\n", @@ -521,7 +522,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This command sends a request with the input message to the running HTTP server. You should be able to see the logs from the pipeline in the console. Please wait until the pipeline execution is finished before proceeding to the next section." + "This command sends a request with the input message to the running HTTP server. You should be able to see the logs from the pipeline in the console." ] }, { @@ -556,14 +557,22 @@ "Vulnerability 'CVE-2024-21762' affected status: FALSE. Label: code_not_present\n", "Vulnerability 'GHSA-hh8p-p8mp-gqhm' affected status: TRUE. Label: vulnerable\n", "Vulnerability 'GHSA-3f63-hfp8-52jq' affected status: FALSE. Label: code_not_reachable\n", - "```" + "```\n", + "\n", + "