diff --git a/garak/probes/transferableattacks.py b/garak/probes/transferableattacks.py
deleted file mode 100644
index 9f708d2af..000000000
--- a/garak/probes/transferableattacks.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""transferableattacks
-
-Creates universal, transferable adversarial attacks using the llm-attacks framework. 
-(code: https://github.com/llm-attacks/llm-attacks/tree/main, paper: https://arxiv.org/abs/2307.15043)
-Code is adapted from the llm-attacks repo to garak probes
-"""
-
-from garak.attempt import Attempt
-from garak.probes.base import Probe
diff --git a/garak/resources/promptinject/llm-attacks.py b/garak/resources/promptinject/llm-attacks.py
deleted file mode 100644
index dbeef951a..000000000
--- a/garak/resources/promptinject/llm-attacks.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# MIT License
-#
-# Copyright (c) 2023 Andy Zou
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""A main script to run attack for LLMs."""
-
-
-import time
-import importlib
-import numpy as np
-import torch.multiprocessing as mp
-from absl import app
-from ml_collections import config_flags
-
-from llm_attacks import get_goals_and_targets, get_workers
-
-_CONFIG = config_flags.DEFINE_config_file("config")
-
-
-# Function to import module at the runtime
-def dynamic_import(module):
-    return importlib.import_module(module)
-
-
-def main(_):
-    mp.set_start_method("spawn")
-
-    params = _CONFIG.value
-
-    attack_lib = dynamic_import(f"llm_attacks.{params.attack}")
-
-    print(params)
-
-    train_goals, train_targets, test_goals, test_targets = get_goals_and_targets(params)
-
-    process_fn = lambda s: s.replace("Sure, h", "H")
-    process_fn2 = lambda s: s.replace("Sure, here is", "Sure, here's")
-    train_targets = [
-        process_fn(t) if np.random.random() < 0.5 else process_fn2(t)
-        for t in train_targets
-    ]
-    test_targets = [
-        process_fn(t) if np.random.random() < 0.5 else process_fn2(t)
-        for t in test_targets
-    ]
-
-    workers, test_workers = get_workers(params)
-
-    managers = {
-        "AP": attack_lib.AttackPrompt,
-        "PM": attack_lib.PromptManager,
-        "MPA": attack_lib.MultiPromptAttack,
-    }
-
-    timestamp = time.strftime("%Y%m%d-%H:%M:%S")
-    if params.transfer:
-        attack = attack_lib.ProgressiveMultiPromptAttack(
-            train_goals,
-            train_targets,
-            workers,
-            progressive_models=params.progressive_models,
-            progressive_goals=params.progressive_goals,
-            control_init=params.control_init,
-            logfile=f"{params.result_prefix}_{timestamp}.json",
-            managers=managers,
-            test_goals=test_goals,
-            test_targets=test_targets,
-            test_workers=test_workers,
-            mpa_deterministic=params.gbda_deterministic,
-            mpa_lr=params.lr,
-            mpa_batch_size=params.batch_size,
-            mpa_n_steps=params.n_steps,
-        )
-    else:
-        attack = attack_lib.IndividualPromptAttack(
-            train_goals,
-            train_targets,
-            workers,
-            control_init=params.control_init,
-            logfile=f"{params.result_prefix}_{timestamp}.json",
-            managers=managers,
-            test_goals=getattr(params, "test_goals", []),
-            test_targets=getattr(params, "test_targets", []),
-            test_workers=test_workers,
-            mpa_deterministic=params.gbda_deterministic,
-            mpa_lr=params.lr,
-            mpa_batch_size=params.batch_size,
-            mpa_n_steps=params.n_steps,
-        )
-    attack.run(
-        n_steps=params.n_steps,
-        batch_size=params.batch_size,
-        topk=params.topk,
-        temp=params.temp,
-        target_weight=params.target_weight,
-        control_weight=params.control_weight,
-        test_steps=getattr(params, "test_steps", 1),
-        anneal=params.anneal,
-        incr_control=params.incr_control,
-        stop_on_success=params.stop_on_success,
-        verbose=params.verbose,
-        filter_cand=params.filter_cand,
-        allow_non_ascii=params.allow_non_ascii,
-    )
-
-    for worker in workers + test_workers:
-        worker.stop()
-
-
-if __name__ == "__main__":
-    app.run(main)