Prompt Leakage effect and defense strategies for multi-turn LLM interactions

• URL: http://arxiv.org/abs/2404.16251v3
• Date: Mon, 29 Jul 2024 17:16:19 GMT
• Title: Prompt Leakage effect and defense strategies for multi-turn LLM interactions
• Authors: Divyansh Agarwal, Alexander R. Fabbri, Ben Risher, Philippe Laban, Shafiq Joty, Chien-Sheng Wu,
• Abstract summary: Leakage of system prompts may compromise intellectual property and act as adversarial reconnaissance for an attacker. We design a unique threat model which leverages the LLM sycophancy effect and elevates the average attack success rate (ASR) from 17.7% to 86.2% in a multi-turn setting. We measure the mitigation effect of 7 black-box defense strategies, along with finetuning an open-source model to defend against leakage attempts.
• Score: 95.33778028192593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt leakage poses a compelling security and privacy threat in LLM applications. Leakage of system prompts may compromise intellectual property, and act as adversarial reconnaissance for an attacker. A systematic evaluation of prompt leakage threats and mitigation strategies is lacking, especially for multi-turn LLM interactions. In this paper, we systematically investigate LLM vulnerabilities against prompt leakage for 10 closed- and open-source LLMs, across four domains. We design a unique threat model which leverages the LLM sycophancy effect and elevates the average attack success rate (ASR) from 17.7% to 86.2% in a multi-turn setting. Our standardized setup further allows dissecting leakage of specific prompt contents such as task instructions and knowledge documents. We measure the mitigation effect of 7 black-box defense strategies, along with finetuning an open-source model to defend against leakage attempts. We present different combination of defenses against our threat model, including a cost analysis. Our study highlights key takeaways for building secure LLM applications and provides directions for research in multi-turn LLM interactions

Related papers

• Cognitive Overload Attack:Prompt Injection for Long Context [39.61095361609769]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in performing tasks without needing explicit retraining. This capability, known as In-Context Learning (ICL), exposes LLMs to adversarial prompts and jailbreaks that manipulate safety-trained LLMs into generating undesired or harmful output. We apply the principles of Cognitive Load Theory in LLMs and empirically validate that similar to human cognition, LLMs also suffer from cognitive overload. We show that advanced models such as GPT-4, Claude-3.5 Sonnet, Claude-3 OPUS, Llama-3-70B-Instruct, Gemini-1.0-Pro, and
  arXiv  Detail & Related papers  (2024-10-15T04:53:34Z)
• MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants [14.947665219536708]
  We introduce the Malicious Programming Prompt (MaPP) attack, in which an attacker adds a small amount of text to a prompt for a programming task. We show that our prompt strategy can cause an LLM to add vulnerabilities while continuing to write otherwise correct code.
  arXiv  Detail & Related papers  (2024-07-12T22:30:35Z)
• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• Purple-teaming LLMs with Adversarial Defender Training [57.535241000787416]
  We present Purple-teaming LLMs with Adversarial Defender training (PAD) PAD is a pipeline designed to safeguard LLMs by novelly incorporating the red-teaming (attack) and blue-teaming (safety training) techniques. PAD significantly outperforms existing baselines in both finding effective attacks and establishing a robust safe guardrail.
  arXiv  Detail & Related papers  (2024-07-01T23:25:30Z)
• A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems [47.18371401090435]
  We analyze the security of Large Language Model (LLM) systems, instead of focusing on the individual LLMs. We propose a multi-layer and multi-step approach and apply it to the state-of-art OpenAI GPT4. We found that although the OpenAI GPT4 has designed numerous safety constraints to improve its safety features, these safety constraints are still vulnerable to attackers.
  arXiv  Detail & Related papers  (2024-02-28T19:00:12Z)
• MART: Improving LLM Safety with Multi-round Automatic Red-Teaming [72.2127916030909]
  We propose a Multi-round Automatic Red-Teaming (MART) method, which incorporates both automatic adversarial prompt writing and safe response generation. On adversarial prompt benchmarks, the violation rate of an LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART. Notably, model helpfulness on non-adversarial prompts remains stable throughout iterations, indicating the target LLM maintains strong performance on instruction following.
  arXiv  Detail & Related papers  (2023-11-13T19:13:29Z)
• Attack Prompt Generation for Red Teaming and Defending Large Language Models [70.157691818224]
  Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. We propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts.
  arXiv  Detail & Related papers  (2023-10-19T06:15:05Z)
• PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models [11.693095252994482]
  We present POISONPROMPT, a novel backdoor attack capable of successfully compromising both hard and soft prompt-based LLMs. Our findings highlight the potential security threats posed by backdoor attacks on prompt-based LLMs and emphasize the need for further research in this area.
  arXiv  Detail & Related papers  (2023-10-19T03:25:28Z)
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks [99.23352758320945]
  We propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks on large language models (LLMs) Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense first randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs.
  arXiv  Detail & Related papers  (2023-10-05T17:01:53Z)
• Goal-Oriented Prompt Attack and Safety Evaluation for LLMs [43.93613764464993]
  We introduce a pipeline to construct high-quality prompt attack samples, along with a Chinese prompt attack dataset called CPAD. Our prompts aim to induce LLMs to generate unexpected outputs with several carefully designed prompt attack templates. The results show that our prompts are significantly harmful to LLMs, with around 70% attack success rate to GPT-3.5.
  arXiv  Detail & Related papers  (2023-09-21T07:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.