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Multi-Turn Context Jailbreak Attack on Large Language Models From First Principles
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Large language models (LLMs) have significantly enhanced the performance of numerous applications, from intelligent conversations to text generation. However, their inherent security vulnerabilities have become an increasingly significant challenge, especially with respect to jailbreak attacks. Attackers can circumvent the security mechanisms of these LLMs, breaching security constraints and causing harmful outputs. Focusing on multi-turn semantic jailbreak attacks, we observe that existing methods lack specific considerations for the role of multiturn dialogues in attack strategies, leading to semantic deviations during continuous interactions. Therefore, in this paper, we establish a theoretical foundation for multi-turn attacks by considering their support in jailbreak attacks, and based on this, propose a context-based contextual fusion black-box jailbreak attack method, named Context Fusion Attack (CFA). This method approach involves filtering and extracting key terms from the target, constructing contextual scenarios around these terms, dynamically integrating the target into the scenarios, replacing malicious key terms within the target, and thereby concealing the direct malicious intent. Through comparisons on various mainstream LLMs and red team datasets, we have demonstrated CFA's superior success rate, divergence, and harmfulness compared to other multi-turn attack strategies, particularly showcasing significant advantages on Llama3 and GPT-4.
Forward citations
Cited by 6 Pith papers
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SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks
Multi-turn LLM jailbreaks succeed based on how harmful intent is organized across turns, not on interaction length, and detection should shift to session and cross-session scope.
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FORTRESS: Frontier Risk Evaluation for National Security and Public Safety
A new benchmark with instance-specific rubrics measures frontier LLMs' willingness to assist with national security and public safety threats, alongside a paired over-refusal test.
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Effective Red-Teaming of Policy-Adherent Agents
A policy-aware red-teaming system (CRAFT) induces policy violations in LLM customer service agents at much higher rates than generic jailbreak prompts, using a new security-focused benchmark (tau-break) built from tau-bench.
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The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
AI safety should be measured by whether deployed systems keep errors visible, contestable, containable, and recoverable across five integrity layers, not only by whether individual model outputs look safe.
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A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.
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Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
A two-stage attack claims to recover a black-box LLM's output projection from under 10k top-k logit queries and distill a compact clone, but the core matrix-completion step is not justified.
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