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EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
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Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibited outputs. However, due to significant differences among various jailbreak methods, there is no standard implementation framework available for the community, which limits comprehensive security evaluations. This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs. It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. This modular framework enables researchers to easily construct attacks from combinations of novel and existing components. So far, EasyJailbreak supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks. Notably, even advanced models like GPT-3.5-Turbo and GPT-4 exhibit average Attack Success Rates (ASR) of 57% and 33%, respectively. We have released a wealth of resources for researchers, including a web platform, PyPI published package, screencast video, and experimental outputs.
Forward citations
Cited by 13 Pith papers
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Evolving Skill-Structured Attack Memory Enhances LLM Jailbreaking
MemoAttack organizes jailbreak experience into evolving skill-structured memories and uses lifecycle management plus contextual Thompson Sampling to achieve 98% ASR on AdvBench while cutting query count by 45.9%.
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SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment
SafeSteer restricts reverse KL penalty to safety tokens selected via activation steering, achieving strong safety on seven benchmarks with minimal degradation on five capability benchmarks using only 100 harmful sampl...
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MT-JailBench: A Modular Benchmark for Understanding Multi-Turn Jailbreak Attacks
MT-JailBench is a modular benchmark that standardizes evaluation of multi-turn jailbreaks to identify key success drivers and enable stronger combined attacks.
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Chain-of-Authorization: Embedding authorization into large language models
LLMs fine-tuned to output authorization trajectories as a prerequisite for responses achieve high rejection rates for unauthorized prompts while preserving utility in allowed scenarios.
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Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
EvoSynth evolves code-based jailbreak algorithms via multi-agent self-correction, reaching 85.5% ASR on Claude-Sonnet-4.5 and 95.9% average across targets with greater diversity.
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IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
IntentionReasoner adds four-level intent classification and targeted query rewriting to LLM guardrails, reporting state-of-the-art harm detection and near-zero jailbreak success.
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Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks
IMAGINE pre-synthesizes intent-concealed jailbreak-like instructions via iterative latent-space expansion, and DPO with that data reduces jailbreak attack success rates on Qwen2.5, Llama3.1 and Llama3.2.
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PUZZLED: Jailbreaking LLMs through Word-Based Puzzles
PUZZLED masks harmful keywords as word-search, anagram, or crossword puzzles and achieves a reported 88.8% average attack success rate across five leading LLMs.
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SoK: Robustness in Large Language Models against Jailbreak Attacks
The paper taxonomizes jailbreak attacks and defenses for LLMs, introduces the Security Cube multi-dimensional evaluation framework, benchmarks 13 attacks and 5 defenses, and identifies open challenges in LLM robustness.
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Lexical Hints of Accuracy in LLM Reasoning Chains
Hesitation words in reasoning chains are claimed to flag incorrect LLM answers, but the manuscript body is a different paper and contains no such study.
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Jailbreak Attacks and Defenses Against Large Language Models: A Survey
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.
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Cybersecurity is the True Frontier for Generative AI Success or Failure
Cybersecurity's scale, adversaries, labeling issues, and operational demands make it the superior test-case for general AI progress over NLP or computer vision.
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.
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