🪤 TRAP Source Code 🍯

Source code of the paper TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification by Martin Gubri, Dennis Ulmer, Hwaran Lee, Sangdoo Yun and Seong Joon Oh.

Developed at Parameter Lab with the support of Naver AI Lab.

🪤 TRAP in a nutshell

🦹 Motivation

💧 Private LLMs that cost millions of dollars to train may be leaked by internal or external threats.
🐍 Open-source LLMs are distributed under restrictive licenses that may not be respected. For instances, Microsoft's Orca-2 is distributed under a no-commercial licence, and Meta's usage policy of Llama-2 forbids deceptive usages.
🎭 LLMs do not disclose reliably their identity. For instances, Mixtral-8x7B identifies it-self as FAIR’s BlenderBot 3.0, and we can disguise GPT-3.5 and GPT-4 as Anthropic's Claude or as Llama-2, using deceptive system prompts.

Therefore, we need specific tools to ensure compliance.

🥷 Problem: Black-Box Identity Verification (BBIV)

A reference LLM (either close or open) can be deployed silently by a third party to power an application. So, we propose a new task, BBIV, of detecting the usage of an LLM in a third-party application, which is critical for assessing compliance.

Question: Does this use our ?

🪤 Solution: Targeted Random Adversarial Prompt (TRAP)

To solve the BBIV problem, we propose a novel method, TRAP, that uses tuned prompt suffixes to reliably force a specific LLM to answer in a pre-defined way.

TRAP is composed of:

a closed-ended question
- 🔥 20 tunable tokens
- ⚙️ optimised on the
- 🎯 to output a specific chosen at random, here 314

🍯 The final prompt is a honeypot:

The suffix forces the reference LLM to output the target number 95-100% of the time
The suffix is specific to the reference LLM (<1% average transfer rate to another LLM)
TRAP beats the perplexity baseline
- Using less output tokens (3-18 tokens vs. 150 tokens)
- Perplexity identification is sensible to the type of prompt

🛡️ Third-party can deploy the with changes

TRAP is robust to generation hyperparameters (usual ranges)
TRAP is not robust to some system prompts

Read the full paper for more details.

Citation

If you use our code or our method, kindly consider citing our paper:

@inproceedings{gubri2024trap,
    title = "{TRAP}: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification",
    author = "Gubri, Martin  and
      Ulmer, Dennis  and
      Lee, Hwaran  and
      Yun, Sangdoo  and
      Oh, Seong Joon",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.683",
    doi = "10.18653/v1/2024.findings-acl.683",
    pages = "11496--11517",
    abstract = "Large Language Model (LLM) services and models often come with legal rules on *who* can use them and *how* they must use them. Assessing the compliance of the released LLMs is crucial, as these rules protect the interests of the LLM contributor and prevent misuse. In this context, we describe the novel fingerprinting problem of Black-box Identity Verification (BBIV). The goal is to determine whether a third-party application uses a certain LLM through its chat function. We propose a method called Targeted Random Adversarial Prompt (TRAP) that identifies the specific LLM in use. We repurpose adversarial suffixes, originally proposed for jailbreaking, to get a pre-defined answer from the target LLM, while other models give random answers. TRAP detects the target LLMs with over 95{\%} true positive rate at under 0.2{\%} false positive rate even after a single interaction. TRAP remains effective even if the LLM has minor changes that do not significantly alter the original function.",
}

Installation

Dependencies

The requirements.txt file corresponds to CUDA version 12.2 and Python 3.8.

pip install -r requirements.txt
pip install -e llm_attacks

If you use another CUDA version, you might need to adapt the requirements, but keep the specified fschat version pip install fschat==0.2.23.

Download models

Set the HUGGINGFACE_HUB_CACHE env variable to your desired folder. Adapt the path in all the code accordingly.

echo "export HUGGINGFACE_HUB_CACHE='/mnt/hdd-nfs/mgubri/models_hf/'" >> ~/.bashrc

# login to HF
huggingface-cli login

# test HF installation
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"

Download models from HuggingFace using python:

from transformers import AutoTokenizer, AutoModelForCausalLM
MODELS_NAMES = [
    "meta-llama/Llama-2-7b-chat-hf", "meta-llama/Llama-2-13b-chat-hf",
    "lmsys/vicuna-7b-v1.3", "lmsys/vicuna-13b-v1.3", 
    "TheBloke/guanaco-7B-HF", "TheBloke/guanaco-13B-HF"
]
for model_name in MODELS_NAMES:
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)

Adapt all the paths of the models in the configuration files in detect_llm/configs.

Download data

Download valid.wp_source from Kaggle, and place it in detect_llm/data/datasets/writing

Experiments

All the following command are executed from the detect_llm folder.

cd detect_llm

Generate prompt and goal strings

python generate_csv.py --n-goals 100 --method random --string-type number --string-length 5 --seed 43  # independent seed to report results 
#python generate_csv.py --n-goals 100 --method random --string-type number --string-length 5 --seed 42  # seed used to debug, and change HPs and XP settings
python generate_csv.py --n-goals 100 --method random --string-type number --string-length 4 --seed 41
python generate_csv.py --n-goals 100 --method random --string-type number --string-length 3 --seed 40

Generate CSV of filtered tokens

See the notebook notebooks/tokenizer_numbers.ipynb

cd data/filter_tokens
ln -s filter_token_number_vicuna.csv filter_token_number_vicuna_guanaco.csv
cd ../..

Optimize the suffixes

Optimize 100 suffixes for the Llama-2-7B-chat, Guanaco-7B, Vicuna-7B models, and the ensemble of both Guanaco-7B and Vicuna-7B, respectively. We use V-100 GPUs to run all the experiments. You will need 32Gb of VRAM to optimize the suffixes for 7B models.

STR_LENGTH=4 #  3  4  5
SEED=41      # 40 41 43
MODEL='llama2' # 'vicuna' 'guanaco' 'vicuna_guanaco'
N_TRAIN_DATA=10
STRING='number'
METHOD='random'
N_STEPS=1500

for DATA_OFFSET in 0 10 20 30 40 50 60 70 80 90 ; do
  sh scripts/run_gcg_individual.sh $MODEL $STRING $METHOD ${STR_LENGTH} ${DATA_OFFSET} ${SEED} ${N_TRAIN_DATA} ${N_STEPS}
done

Compute true positive and false positive rates on open models

We compute the true positive rate, i.e., the probability that the reference model retrieves the targeted answer, and the false positive rate, i.e., the probability that another model provides the targeted answer. We generate 10 answers for each suffix and compute the overall average.

Table of transferability.

str_length=4 # 3 4 5
EXPORT_PATH="/mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/results/method_random/type_number/str_length_${str_length}/transferability/retrieval_rate_table.csv"
SUFFIX_MODELS=(
  "vicuna" 
  "guanaco" 
  "llama2" 
  "vicuna_guanaco"
)
TARGET_MODELS=(
  "vicuna vicuna-7B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-7b-v1.3/snapshots/236eeeab96f0dc2e463f2bebb7bb49809279c6d6/"
  "vicuna vicuna-13B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-13b-v1.3/snapshots/6566e9cb1787585d1147dcf4f9bc48f29e1328d2/"
  "llama-2 llama2-7B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/"
  "llama-2 llama2-13B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-13b-chat-hf/snapshots/c2f3ec81aac798ae26dcc57799a994dfbf521496/"
  "guanaco guanaco-7B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-7B-HF/snapshots/293c24105fa15afa127a2ec3905fdc2a0a3a6dac/"
  "guanaco guanaco-13B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-13B-HF/snapshots/bd59c700815124df616a17f5b49a0bc51590b231/"
)
for suffix_model in "${SUFFIX_MODELS[@]}"; do
    SUFFIX_PATH="/mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/results/method_random/type_number/str_length_${str_length}/model_${suffix_model}" 
    for target_model in "${TARGET_MODELS[@]}"; do
      IFS=' ' read -r target_name target_version target_path <<< "$target_model"
      echo "**** FROM $suffix_model TO $target_version ****"
      python -u compute_results.py --path-suffixes ${SUFFIX_PATH} --model-name $target_name --model-version $target_version --model-path $target_path --export-csv ${EXPORT_PATH} --verbose 1 
    done
done

Compute false positive rate on close models

We also generate 10 answers per model and per suffix. We use the same generation hyperparameter as the previous section.

str_length=4 # 3 4 5
N=10
for MODEL in 'llama2' 'vicuna' 'guanaco' 'vicuna_guanaco' ; do
    PATH_SUFFIXES="results/method_random/type_number/str_length_${str_length}/model_${MODEL}"
    # openai
    python get_answer_api.py --path-suffixes ${PATH_SUFFIXES} --n-gen 10 --model-name 'gpt-3.5-turbo-0613' --api-name 'openai' --gen-config-override "{'temperature': [0.6], 'top_p': [0.9]}"
    python get_answer_api.py --path-suffixes ${PATH_SUFFIXES} --n-gen 10 --model-name 'gpt-4-1106-preview' --api-name 'openai' --gen-config-override "{'temperature': [0.6], 'top_p': [0.9]}"
    # claude
    python get_answer_api.py --path-suffixes ${PATH_SUFFIXES} --n-gen 10 --model-name 'claude-2.1' --api-name 'anthropic' --gen-config-override "{'temperature': [0.6], 'top_p': [0.9]}"
    python get_answer_api.py --path-suffixes ${PATH_SUFFIXES} --n-gen 10 --model-name 'claude-instant-1.2' --api-name 'anthropic' --gen-config-override "{'temperature': [0.6], 'top_p': [0.9]}" 
done

Robustness

We compute the robustness of the true positive rate with respect to changes to the reference model.

PATH_LLAMA='/mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/'
PATH_SUFFIXES="/mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/results/method_random/type_number/str_length_4/model_llama2"

Generation hyperparameters

Temperature

for temp in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 ; do
    echo "*** Temperature: ${temp} ***"
    NEW_GEN_CONF="{'temperature': ${temp}}"
    python compute_results.py --path-suffixes ${PATH_SUFFIXES} --model-name llama-2 --model-path ${PATH_LLAMA} --verbose 2 --gen-config-override "${NEW_GEN_CONF}"
done

Top-p

for top_p in 1.0 0.9901107197234477 0.979243460803013 0.9673015081895581 0.9541785824116654 0.939757893723113 0.9239111027123406 0.9064971781236734 0.8873611417252854 0.8663326890536376 0.8432246737594684 0.8178314420665103 0.789927002520161 0.7592630147374724 0.7255665792589857 0.6885378088328238 0.6478471595162576 0.6031324978424272 0.5539958779509593 0.5 ; do
    echo "*** Top-p: ${top_p} ***"
    NEW_GEN_CONF="{'top_p': ${top_p}}"
    python compute_results.py --path-suffixes ${PATH_SUFFIXES} --model-name llama-2 --model-path ${PATH_LLAMA} --verbose 2 --gen-config-override "${NEW_GEN_CONF}"
done

Top-p values on log scale generated with:

import numpy as np
1.1-np.logspace(np.log10(0.1), np.log10(0.6), 20)
' '.join([str(x) for x in (1.1-np.logspace(np.log10(0.1), np.log10(0.6), 20)).tolist()])

System prompt

python compute_results.py --path-suffixes ${PATH_SUFFIXES} --model-name llama-2 --model-path ${PATH_LLAMA} --system-prompt all

Baselines

1. Sample answers

Sample 10k answers without suffixes for every open models.

SEED=70
TARGET_MODELS=(
  "llama-2 llama2-7B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/"
  "llama-2 llama2-13B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-13b-chat-hf/snapshots/c2f3ec81aac798ae26dcc57799a994dfbf521496/"
  "vicuna vicuna-7B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-7b-v1.3/snapshots/236eeeab96f0dc2e463f2bebb7bb49809279c6d6/"
  "vicuna vicuna-13B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-13b-v1.3/snapshots/6566e9cb1787585d1147dcf4f9bc48f29e1328d2/"
  "guanaco guanaco-7B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-7B-HF/snapshots/293c24105fa15afa127a2ec3905fdc2a0a3a6dac/"
  "guanaco guanaco-13B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-13B-HF/snapshots/bd59c700815124df616a17f5b49a0bc51590b231/"
)

for target_model in "${TARGET_MODELS[@]}"; do
  IFS=' ' read -r target_name target_version target_path <<< "$target_model"
  echo "***** MODEL $target_version *****"
  for temp in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 ; do
    echo "** Temperature: ${temp} **"
    NEW_GEN_CONF="{'temperature': ${temp}, 'top_p':1.0}"
    python -u compute_results_baseline.py --n-gen 10000 --n-digits 4 --model-name $target_name --model-version $target_version --model-path $target_path --verbose 2 --export-base-folder /mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/ --export-sub-folder 'xp_temperature' --gen-config-override "${NEW_GEN_CONF}" --seed $SEED
  done
done

Sample from OpenAI API.

OPENAI_MODELS=(
  "gpt-3.5-turbo-0613"
  "gpt-4-1106-preview"
)

for model in "${OPENAI_MODELS[@]}"; do
  echo "**** MODEL $model ****"
  python -m pdb compute_results_baseline_api.py --api 'openai' --model-name $model --n-gen 10000 --n-digits 4 --system-prompt 'openai' --verbose 2 --export-base-folder . 
done

Sample open models with different system prompts.

SEED=70
TARGET_MODELS=(
  "llama-2 llama2-7B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/"
  "llama-2 llama2-13B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-13b-chat-hf/snapshots/c2f3ec81aac798ae26dcc57799a994dfbf521496/"
  "vicuna vicuna-7B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-7b-v1.3/snapshots/236eeeab96f0dc2e463f2bebb7bb49809279c6d6/"
  "vicuna vicuna-13B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-13b-v1.3/snapshots/6566e9cb1787585d1147dcf4f9bc48f29e1328d2/"
  "guanaco guanaco-7B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-7B-HF/snapshots/293c24105fa15afa127a2ec3905fdc2a0a3a6dac/"
  "guanaco guanaco-13B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-13B-HF/snapshots/bd59c700815124df616a17f5b49a0bc51590b231/"
)

for target_model in "${TARGET_MODELS[@]}"; do
    IFS=' ' read -r target_name target_version target_path <<< "$target_model"
    echo "***** MODEL $target_version *****"
    for scenario in 'llama-2' 'openai' 'fastchat' 'SHAKESPEARE_WRITING_ASSISTANT' 'IRS_TAX_CHATBOT' 'MARKETING_WRITING_ASSISTANT' 'XBOX_CUSTOMER_SUPPORT_AGENT' 'HIKING_RECOMMENDATION_CHATBOT' 'JSON_FORMATTER_ASSISTANT' ; do
        echo "** Scenario system prompt: ${scenario} **"
        temp='1.0'
        NEW_GEN_CONF="{'temperature': ${temp}, 'top_p':1.0}"
        python -u compute_results_baseline.py --n-gen 10000 --n-digits 4 --model-name $target_name --model-version $target_version --model-path $target_path --verbose 2 --export-base-folder /mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/ --export-sub-folder 'xp_system_prompt' --gen-config-override "${NEW_GEN_CONF}" --seed $SEED --system-prompt "${scenario}"
    done
done

2. Perplexity

First, we generate completions from 10 models using the same prompts across three datasets, with 1000 prompts for each dataset. Each prompt dataset is a different style.

Close models

for DATASET in 'writing' 'pubmed' 'wiki' ; do
    echo "===== Prompts $DATASET ====="
    # openai models
    python baseline_ppl.py gen --dataset=$DATASET --n-prompts=1000 --seed=0 --api openai --model-name gpt-3.5-turbo-0613
    python baseline_ppl.py gen --dataset=$DATASET --n-prompts=1000 --seed=0 --api openai --model-name gpt-4-1106-preview
    # anthropic
    python baseline_ppl.py gen --dataset=$DATASET --n-prompts=1000 --seed=0 --api anthropic --model-name claude-instant-1.2
    python baseline_ppl.py gen --dataset=$DATASET --n-prompts=1000 --seed=0 --api anthropic --model-name claude-2.1
done

Open models

# launch with env variables in scripts/hyperparameters/baseline_ppl_gen.csv
echo "***** MODEL ${model_version} *****"
python baseline_ppl.py gen --dataset=$DATASET --n-prompts=1000 --seed=0 --model-path "${model_path}" --model-name "${model_version}" --export-base-folder '/mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm'

Second, we compute the perplexity of the previously generated texts on the three reference models:

# open models
GEN_MODELS=(
  "llama2-7B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/"
  "llama2-13B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-13b-chat-hf/snapshots/c2f3ec81aac798ae26dcc57799a994dfbf521496/"
  "vicuna-7B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-7b-v1.3/snapshots/236eeeab96f0dc2e463f2bebb7bb49809279c6d6/"
  "vicuna-13B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-13b-v1.3/snapshots/6566e9cb1787585d1147dcf4f9bc48f29e1328d2/"
  "guanaco-7B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-7B-HF/snapshots/293c24105fa15afa127a2ec3905fdc2a0a3a6dac/"
  "guanaco-13B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-13B-HF/snapshots/bd59c700815124df616a17f5b49a0bc51590b231/"
)
# close models
GEN_MODELS=("gpt-3.5-turbo-0613" "gpt-4-1106-preview" "claude-instant-1.2" "claude-2.1")

EVAL_MODELS=(
  "llama2-7B /mnt/hdd-nfs/mgubri/models_hf/models--meta-llama--Llama-2-7b-chat-hf/snapshots/08751db2aca9bf2f7f80d2e516117a53d7450235/"
  "vicuna-7B /mnt/hdd-nfs/mgubri/models_hf/models--lmsys--vicuna-7b-v1.3/snapshots/236eeeab96f0dc2e463f2bebb7bb49809279c6d6/"
  "guanaco-7B /mnt/hdd-nfs/mgubri/models_hf/models--TheBloke--guanaco-7B-HF/snapshots/293c24105fa15afa127a2ec3905fdc2a0a3a6dac/"
)
DATASETS=('writing' 'pubmed' 'wiki')
for dataset in "${DATASETS[@]}" ; do
  echo "======= DATASET ${dataset} ========"
  for gen_model in "${GEN_MODELS[@]}"; do
    IFS=' ' read -r gen_model_version gen_model_path <<< "$gen_model"
    echo "**** GEN model ${gen_model_version} ****"
    PATH_GEN="/mnt/hdd-nfs/mgubri/adv-suffixes/detect_llm/results/baseline/ppl/dataset_${dataset}/gen_model_${gen_model_version}/gen_texts_n1000_system_prompt_original_temperature_0.6_top_p_0.9_seed0.csv"
    for eval_model in "${EVAL_MODELS[@]}"; do
      IFS=' ' read -r eval_model_version eval_model_path <<< "$eval_model"
      echo "** EVAL model ${eval_model_version} **"
      python baseline_ppl.py eval --dataset="${dataset}" --seed=0 --model-path "${eval_model_path}" --model-name "${eval_model_version}" --gen-csv "${PATH_GEN}"
    done
  done
done

Analysis

notebooks/analyse_results.ipynb contains Python code to parse the results of the optimization of suffixes

Reproducibility

To ease future research, we release the CSV containing our optimized suffixes in results/method_random/type_number/str_length_{str_length}/model_{model}/suffixes.csv.

Credits

The code is under MIT licence.

The code in llm_attacks is derived from the source code of the paper "Universal and Transferable Adversarial Attacks on Aligned Language Models" by Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson.
The writing preprocessing code of load_writing() in baseline_ppl.py is derived from "DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature".
The code in detect_llm was partially developed with GPT-4 as a coding assistant.

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
detect_llm		detect_llm
img		img
llm_attacks		llm_attacks
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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Repository files navigation

🪤 TRAP Source Code 🍯

Table of Contents

🪤 TRAP in a nutshell

🦹 Motivation

🥷 Problem: Black-Box Identity Verification (BBIV)

🪤 Solution: Targeted Random Adversarial Prompt (TRAP)

Citation

Installation

Dependencies

Download models

Download data

Experiments

Generate prompt and goal strings

Generate CSV of filtered tokens

Optimize the suffixes

Compute true positive and false positive rates on open models

Compute false positive rate on close models

Robustness

Generation hyperparameters

System prompt

Baselines

1. Sample answers

2. Perplexity

Analysis

Reproducibility

Credits

About

Releases

Packages

Languages

License

parameterlab/trap

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Repository files navigation

🪤 TRAP Source Code 🍯

Table of Contents

🪤 TRAP in a nutshell

🦹 Motivation

🥷 Problem: Black-Box Identity Verification (BBIV)

🪤 Solution: Targeted Random Adversarial Prompt (TRAP)

Citation

Installation

Dependencies

Download models

Download data

Experiments

Generate prompt and goal strings

Generate CSV of filtered tokens

Optimize the suffixes

Compute true positive and false positive rates on open models

Compute false positive rate on close models

Robustness

Generation hyperparameters

System prompt

Baselines

1. Sample answers

2. Perplexity

Analysis

Reproducibility

Credits

About

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Resources

License

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