ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs

• URL: http://arxiv.org/abs/2502.13162v1
• Date: Sun, 16 Feb 2025 18:47:41 GMT
• Title: ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs
• Authors: Ziyi Ni, Hao Wang, Huacan Wang,
• Abstract summary: ShieldLearner is a novel paradigm that mimics human learning in defense. Through trial and error, it autonomously distills attack signatures into a Pattern Atlas. Adaptive Adversarial Augmentation generates adversarial variations of successfully defended prompts.
• Score: 4.534938642552179
• License:
• Abstract: Large Language Models (LLMs) have achieved remarkable success in various domains but remain vulnerable to adversarial jailbreak attacks. Existing prompt-defense strategies, including parameter-modifying and parameter-free approaches, face limitations in adaptability, interpretability, and customization, constraining their effectiveness against evolving threats. To address these challenges, we propose ShieldLearner, a novel paradigm that mimics human learning in defense. Through trial and error, it autonomously distills attack signatures into a Pattern Atlas and synthesizes defense heuristics into a Meta-analysis Framework, enabling systematic and interpretable threat detection. Furthermore, we introduce Adaptive Adversarial Augmentation to generate adversarial variations of successfully defended prompts, enabling continuous self-improvement without model retraining. In addition to standard benchmarks, we create a hard test set by curating adversarial prompts from the Wildjailbreak dataset, emphasizing more concealed malicious intent. Experimental results show that ShieldLearner achieves a significantly higher defense success rate than existing baselines on both conventional and hard test sets, while also operating with lower computational overhead, making it a practical and efficient solution for real-world adversarial defense.

Related papers

• FlexLLM: Exploring LLM Customization for Moving Target Defense on Black-Box LLMs Against Jailbreak Attacks [7.31505609352525]
  Defense in large language models (LLMs) is crucial to counter the numerous attackers exploiting these systems to generate harmful content. We propose a moving target defense approach that alters decoding hyper parameters to enhance model robustness. Our results demonstrate that our defense is the most effective against jailbreak attacks in three of the models tested.
  arXiv  Detail & Related papers  (2024-12-10T17:02:28Z)
• Sustainable Self-evolution Adversarial Training [51.25767996364584]
  We propose a Sustainable Self-Evolution Adversarial Training (SSEAT) framework for adversarial training defense models. We introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples. We also propose an adversarial data replay module to better select more diverse and key relearning data.
  arXiv  Detail & Related papers  (2024-12-03T08:41:11Z)
• Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs [13.317364896194903]
  We propose a two-stage adversarial tuning framework to enhance Large Language Models' generalized defense capabilities. In the first stage, we introduce the hierarchical meta-universal adversarial prompt learning to efficiently generate token-level adversarial prompts. In the second stage, we propose the automatic adversarial prompt learning to iteratively refine semantic-level adversarial prompts.
  arXiv  Detail & Related papers  (2024-06-07T15:37:15Z)
• AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
  We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques. We propose three comprehensive, automated, and logical frameworks. We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
  arXiv  Detail & Related papers  (2024-06-06T07:24:41Z)
• Fight Back Against Jailbreaking via Prompt Adversarial Tuning [23.55544992740663]
  Large Language Models (LLMs) are susceptible to jailbreaking attacks. We propose an approach named Prompt Adversarial Tuning (PAT) that trains a prompt control attached to the user prompt as a guard prefix. Our method is effective against both grey-box and black-box attacks, reducing the success rate of advanced attacks to nearly 0%.
  arXiv  Detail & Related papers  (2024-02-09T09:09:39Z)
• 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)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)

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.