Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment

• URL: http://arxiv.org/abs/2311.09433v3
• Date: Thu, 15 Aug 2024 19:51:07 GMT
• Title: Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment
• Authors: Haoran Wang, Kai Shu,
• Abstract summary: We study an attack scenario called Trojan Activation Attack (TA2), which injects trojan steering vectors into the activation layers of Large Language Models. Our experiment results show that TA2 is highly effective and adds little or no overhead to attack efficiency.
• Score: 31.24530091590395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To ensure AI safety, instruction-tuned Large Language Models (LLMs) are specifically trained to ensure alignment, which refers to making models behave in accordance with human intentions. While these models have demonstrated commendable results on various safety benchmarks, the vulnerability of their safety alignment has not been extensively studied. This is particularly troubling given the potential harm that LLMs can inflict. Existing attack methods on LLMs often rely on poisoned training data or the injection of malicious prompts. These approaches compromise the stealthiness and generalizability of the attacks, making them susceptible to detection. Additionally, these models often demand substantial computational resources for implementation, making them less practical for real-world applications. In this work, we study a different attack scenario, called Trojan Activation Attack (TA^2), which injects trojan steering vectors into the activation layers of LLMs. These malicious steering vectors can be triggered at inference time to steer the models toward attacker-desired behaviors by manipulating their activations. Our experiment results on four primary alignment tasks show that TA^2 is highly effective and adds little or no overhead to attack efficiency. Additionally, we discuss potential countermeasures against such activation attacks.

Related papers

• Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks [16.508109544083496]
  Vision Language Models (VLMs) can produce unintended and harmful content when exposed to adversarial attacks. Existing defenses, such as input preprocessing, adversarial training, and response evaluation-based methods, are often impractical for real-world deployment. We propose ASTRA, an efficient and effective defense by adaptively steering models away from adversarial feature directions to resist VLM attacks.
  arXiv  Detail & Related papers  (2024-11-23T02:17:17Z)
• Defense Against Prompt Injection Attack by Leveraging Attack Techniques [66.65466992544728]
  Large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks. As LLMs continue to evolve, new vulnerabilities, especially prompt injection attacks arise. Recent attack methods leverage LLMs' instruction-following abilities and their inabilities to distinguish instructions injected in the data content.
  arXiv  Detail & Related papers  (2024-11-01T09:14:21Z)
• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models [8.024771725860127]
  Large Language Models (LLMs) remain vulnerable to jailbreak attacks that bypass their safety mechanisms. We introduce a novel scalable jailbreak attack that preempts the activation of an LLM's safety policies by occupying its computational resources.
  arXiv  Detail & Related papers  (2024-10-05T15:10:01Z)
• Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification [35.16099878559559]
  Large language models (LLMs) have experienced significant development and are being deployed in real-world applications. We introduce a new type of attack that causes malfunctions by misleading the agent into executing repetitive or irrelevant actions. Our experiments reveal that these attacks can induce failure rates exceeding 80% in multiple scenarios.
  arXiv  Detail & Related papers  (2024-07-30T14:35:31Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• Uncovering Safety Risks of Large Language Models through Concept Activation Vector [13.804245297233454]
  We introduce a Safety Concept Activation Vector (SCAV) framework to guide attacks on large language models (LLMs) We then develop an SCAV-guided attack method that can generate both attack prompts and embedding-level attacks. Our attack method significantly improves the attack success rate and response quality while requiring less training data.
  arXiv  Detail & Related papers  (2024-04-18T09:46:25Z)
• InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance [56.184255657175335]
  We develop textbfInferAligner, a novel inference-time alignment method that utilizes cross-model guidance for harmlessness alignment. Experimental results show that our method can be very effectively applied to domain-specific models in finance, medicine, and mathematics. It significantly diminishes the Attack Success Rate (ASR) of both harmful instructions and jailbreak attacks, while maintaining almost unchanged performance in downstream tasks.
  arXiv  Detail & Related papers  (2024-01-20T10:41:03Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models [64.79319733514266]
  Adversarial attacks can introduce subtle perturbations to input data. Recent attack methods can achieve a relatively high attack success rate (ASR) We propose a Distribution-Aware LoRA-based Adversarial Attack (DALA) method.
  arXiv  Detail & Related papers  (2023-11-14T23:43:47Z)

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.