LostOxygen / llm-confidentiality

Whispers in the Machine: Confidentiality in LLM-integrated Systems

Whispers in the Machine: Confidentiality in LLM-integrated Systems

This is the code repository accompanying our paper Whispers in the Machine: Confidentiality in LLM-integrated Systems.

Large Language Models (LLMs) are increasingly integrated with external tools. While these integrations can significantly improve the functionality of LLMs, they also create a new attack surface where confidential data may be disclosed between different components. Specifically, malicious tools can exploit vulnerabilities in the LLM itself to manipulate the model and compromise the data of other services, raising the question of how private data can be protected in the context of LLM integrations.

In this work, we provide a systematic way of evaluating confidentiality in LLM-integrated systems. For this, we formalize a "secret key" game that can capture the ability of a model to conceal private information. This enables us to compare the vulnerability of a model against confidentiality attacks and also the effectiveness of different defense strategies. In this framework, we evaluate eight previously published attacks and four defenses. We find that current defenses lack generalization across attack strategies. Building on this analysis, we propose a method for robustness fine-tuning, inspired by adversarial training.
This approach is effective in lowering the success rate of attackers and in improving the system's resilience against unknown attacks.

If you want to cite our work, please use the this BibTeX entry.

This framework was developed to study the confidentiality of Large Language Models (LLMs). The framework contains several features:

• A set of attacks against LLMs, where the LLM is not allowed to leak a secret key -> jump
• A set of defenses against the aforementioned attacks -> jump
• Creating enhanced system prompts to safely instruct an LLM to keep a secret key safe -> jump
• Finetune LLMs to harden them against these attacks using the datasets -> jump

Warning

Hardware aceleration is only fully supported for CUDA machines running Linux. Windows or MacOS with CUDA/MPS could face some issues.

Setup

Before running the code, install the requirements:

python -m pip install --upgrade -r requirements.txt

Create both a key.txt file containing your OpenAI API key as well as a hf_token.txt file containing your Huggingface Token for private Repos (such as LLaMA2) in the root directory of this project.

Sometimes it can be necessary to login to your Huggingface account via the CLI:

git config --global credential.helper store
huggingface-cli login

Distributed Training

All scripts are able to work on multiple GPUs/CPUs using the accelerate library. To do so, run:

accelerate config

to configure the distributed training capabilities of your system and start the scripts with:

accelerate launch [parameters] <script.py> [script parameters]

Attacks and Defenses

Usage

python attack.py [-h] [-a | --attacks [ATTACK1, ATTACK2, ..]] [-d | --defense DEFENSE] [-llm | --llm_type LLM_TYPE] [-m | --iterations ITERATIONS] [-t | --temperature TEMPERATURE]

Example Usage

python attack.py --attacks "payload_splitting" "obfuscation" --defense "xml_tagging" --iterations 15 --llm_type "llama2-7b" --temperature 0.7

Arguments

Argument	Type	Default Value	Description
-h, --help	-	-	show this help message and exit
-a, --attacks	List[str]	payload_splitting	specifies the attacks which will be utilized against the LLM
-d, --defense	str	None	specifies the defense for the LLM
-llm, --llm_type	str	gpt-3.5-turbo	specifies the type of opponent
-le, --llm_guessing	bool	False	specifies whether a second LLM is used to guess the secret key off the normal response or not
-t, --temperature	float	0.0	specifies the temperature for the LLM to control the randomness
-cp, --create_prompt_dataset	bool	False	specifies whether a new dataset of enhanced system prompts should be created
-cr, --create_response_dataset	bool	False	specifies whether a new dataset of secret leaking responses should be created
-i, --iterations	int	10	specifies the number of iterations for the attack
-n, --name_suffix	str	""	Specifies a name suffix to load custom models. Since argument parameter strings aren't allowed to start with '-' symbols, the first '-' will be added by the parser automatically
-s, --strategy	str	None	Specifies the strategy for the attack (whether to use normal attacks or tools attacks)
-sc, --scenario	str	all	Specifies the scenario for the tool based attacks
-dx, --device	str	cpu	Specifies the device which is used for running the script (cpu, cuda, or mps)
-pf, --prompt_format	str	react	Specifies whether react or tool-finetuned prompt format is used for agents. (react or tool-finetuned)
-ds, --disable_safeguards	bool	False	Disables system prompt safeguards for tool strategy
The naming conventions for the models are as follows:

<model_name>-<param_count>-<robustness>-<attack_suffix>-<custom_suffix>

e.g.:

llama2-7b-robust-prompt_injection-0613

If you want to run the attacks against a prefix-tuned model with a custom suffix (e.g., 1000epochs), you would have to specify the arguments a follows:

... --model_name llama2-7b-prefix --name_suffix 1000epochs ...

Supported Large Language Models

Model	Parameter Specifier	Link	Compute Instance
GPT-4 (o1, o1-mini, turbo)	gpt-4o / gpt-4o-mini / gpt-4-turbo	Link	OpenAI API
LLaMA 2	llama2-7b / llama2-13b / llama2-70b	Link	Local Inference
LLaMA 2 hardened	llama2-7b-robust / llama2-13b-robust / llama2-70b-robust	Link	Local Inference
Llama 3.1	llama3-8b / llama3-70b	Link	Local Inference (first: ollama pull llama3.1/llama3.1:70b/llama3.1:405b)
Llama 3.2	llama3-1b/ llama3-3b	Link	Local Inference (first: ollama pull llama3.2/llama3.2:1b)
Reflection Llama	reflection-llama	Link	Local Inference (first: ollama pull reflection)
Vicuna	vicuna-7b / vicuna-13b / vicuna-33b	Link	Local Inference
StableBeluga (2)	beluga-7b / beluga-13b / beluga2-70b	Link	Local Inference
Orca 2	orca2-7b / orca2-13b / orca2-70b	Link	Local Inference
Gemma	gemma-2b / gemma-7b	Link	Local Inference
Gemma 2	gemma2-9b / gemma2-27b	Link	Local Inference (first: ollama pull gemma2/gemma2:27b)
Phi 3	phi3-3b / phi3-14b	Link	Local Inference (first: ollama pull phi3:mini/phi3:medium)

(Finetuned or robust/hardened LLaMA models first have to be generated using the finetuning.py script, see below)

Supported Attacks and Defenses

Attacks		Defenses
Name	Specifier	Name	Specifier
Payload Splitting	payload_splitting	Random Sequence Enclosure	seq_enclosure
Obfuscation	obfuscation	XML Tagging	xml_tagging
Jailbreak	jailbreak	Heuristic/Filtering Defense	heuristic_defense
Translation	translation	Sandwich Defense	sandwiching
ChatML Abuse	chatml_abuse	LLM Evaluation	llm_eval
Masking	masking	Perplexity Detection	ppl_detection
Typoglycemia	typoglycemia	PromptGuard	prompt_guard
Adversarial Suffix	advs_suffix
Prefix Injection	prefix_injection
Refusal Suppression	refusal_suppression
Context Ignoring	context_ignoring
Context Termination	context_termination
Context Switching Separators	context_switching_separators
Few-Shot	few_shot
Cognitive Hacking	cognitive_hacking
Base Chat	base_chat

The base_chat attack consists of normal questions to test of the model spills it's context and confidential information even without a real attack.

---

Finetuning

This section covers the possible LLaMA finetuning options. We use PEFT, which is based on this paper.

Setup

Additionally to the above setup run

accelerate config

to configure the distributed training capabilities of your system. And

wandb login

with your WandB API key to enable logging of the finetuning process.

---

Parameter Efficient Finetuning to harden LLMs against attacks or create enhanced system prompts

The first finetuning option is on a dataset consisting of system prompts to safely instruct an LLM to keep a secret key safe. The second finetuning option (using the --train_robust option) is using system prompts and adversarial prompts to harden the model against prompt injection attacks.

Usage

python finetuning.py [-h] [-llm | --llm_type LLM_NAME] [-i | --iterations ITERATIONS] [-a | --attacks ATTACKS_LIST] [-n | --name_suffix NAME_SUFFIX]

Arguments

Argument	Type	Default Value	Description
-h, --help	-	-	Show this help message and exit
-llm, --llm_type	str	llama3-8b	Specifies the type of llm to finetune
-i, --iterations	int	10000	Specifies the number of iterations for the finetuning
-advs, --advs_train	bool	False	Utilizes the adversarial training to harden the finetuned LLM
-a, --attacks	List[str]	payload_splitting	Specifies the attacks which will be used to harden the llm during finetuning. Only has an effect if --train_robust is set to True. For supported attacks see the previous section
-n, --name_suffix	str	""	Specifies a suffix for the finetuned model name

Supported Large Language Models

Currently only the LLaMA models are supported (llama2-7/13/70b / llama3-8/70b).

Generate System Prompt Datasets

Simply run the generate_dataset.py script to create new system prompts as a json file using LLMs.

Arguments

Argument	Type	Default Value	Description
-h, --help	-	-	Show this help message and exit
-llm, --llm_type	str	llama3-70b	Specifies the LLM used to generate the system prompt dataset
-n, --name_suffix	str	""	Specifies a suffix for the model name if you want to use a custom model
-ds, --dataset_size	int	1000	Size of the resulting system prompt dataset

Citation

If you want to cite our work, please use the following BibTeX entry:

@article{evertz-24-whispers,
	title    =  {{Whispers in the Machine: Confidentiality in LLM-integrated Systems}}, 
	author   =  {Jonathan Evertz and Merlin Chlosta and Lea Schönherr and Thorsten Eisenhofer},
	year     =  {2024},
	journal  =  {Computing Research Repository (CoRR)}
}