Robust LLM safeguarding via refusal feature adversarial training

• URL: http://arxiv.org/abs/2409.20089v1
• Date: Mon, 30 Sep 2024 08:41:39 GMT
• Title: Robust LLM safeguarding via refusal feature adversarial training
• Authors: Lei Yu, Virginie Do, Karen Hambardzumyan, Nicola Cancedda,
• Abstract summary: Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses. We propose Refusal Feature Adrial Training (ReFAT), a novel algorithm that efficiently performs adversarial training. Experiment results show that ReFAT significantly improves the robustness of three popular LLMs against a wide range of adversarial attacks.
• Score: 15.76605079209956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses. Defending against such attacks remains challenging due to the opacity of jailbreaking mechanisms and the high computational cost of training LLMs robustly. We demonstrate that adversarial attacks share a universal mechanism for circumventing LLM safeguards that works by ablating a dimension in the residual stream embedding space called the refusal feature. We further show that the operation of refusal feature ablation (RFA) approximates the worst-case perturbation of offsetting model safety. Based on these findings, we propose Refusal Feature Adversarial Training (ReFAT), a novel algorithm that efficiently performs LLM adversarial training by simulating the effect of input-level attacks via RFA. Experiment results show that ReFAT significantly improves the robustness of three popular LLMs against a wide range of adversarial attacks, with considerably less computational overhead compared to existing adversarial training methods.

Related papers

• Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities [63.603861880022954]
  We introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. It exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3.
  arXiv  Detail & Related papers  (2024-10-24T06:36:12Z)
• Defending against Reverse Preference Attacks is Difficult [26.872318173182414]
  Large Language Models (LLMs) are vulnerable to training-time attacks such as supervised fine-tuning (SFT) on harmful datasets. We propose Reverse Preference Attacks (RPA) to make LLMs learn harmful behavior using adversarial reward during reinforcement learning from human feedback.
  arXiv  Detail & Related papers  (2024-09-19T17:10:34Z)
• Tamper-Resistant Safeguards for Open-Weight LLMs [57.90526233549399]
  We develop a method for building tamper-resistant safeguards into open-weight LLMs. We find that our method greatly improves tamper-resistance while preserving benign capabilities. Our results demonstrate that tamper-resistance is a tractable problem.
  arXiv  Detail & Related papers  (2024-08-01T17:59:12Z)
• 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)
• Defending Large Language Models Against Attacks With Residual Stream Activation Analysis [0.0]
  Large Language Models (LLMs) are vulnerable to adversarial threats. This paper presents an innovative defensive strategy, given white box access to an LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification.
  arXiv  Detail & Related papers  (2024-06-05T13:06:33Z)
• Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning [21.018893978967053]
  In-Context Learning (ICL) is sensitive to the choice, order, and verbaliser used to encode the demonstrations in the prompt. Retrieval-Augmented ICL methods try to address this problem by leveraging retrievers to extract semantically related examples as demonstrations. Our study reveals that retrieval-augmented models can enhance robustness against test sample attacks. We introduce an effective training-free adversarial defence method, DARD, which enriches the example pool with those attacked samples.
  arXiv  Detail & Related papers  (2024-05-24T23:56:36Z)
• Efficient Adversarial Training in LLMs with Continuous Attacks [99.5882845458567]
  Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses. C-AdvIPO is an adversarial variant of IPO that does not require utility data for adversarially robust alignment.
  arXiv  Detail & Related papers  (2024-05-24T14:20:09Z)
• ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings [58.82536530615557]
  We propose an Adversarial Suffix Embedding Translation Framework (ASETF) to transform continuous adversarial suffix embeddings into coherent and understandable text. Our method significantly reduces the computation time of adversarial suffixes and achieves a much better attack success rate to existing techniques.
  arXiv  Detail & Related papers  (2024-02-25T06:46:27Z)
• Learning to Poison Large Language Models During Instruction Tuning [12.521338629194503]
  This work identifies additional security risks in Large Language Models (LLMs) by designing a new data poisoning attack tailored to exploit the instruction tuning process. We propose a novel gradient-guided backdoor trigger learning (GBTL) algorithm to identify adversarial triggers efficiently. We propose two defense strategies against data poisoning attacks, including in-context learning (ICL) and continuous learning (CL)
  arXiv  Detail & Related papers  (2024-02-21T01:30:03Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [82.98081731588717]
  Integration of large language models with external content exposes applications to indirect prompt injection attacks. We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to evaluate the risk of such attacks. We develop two black-box methods based on prompt learning and a white-box defense method based on fine-tuning with adversarial training.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)

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.