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Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
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Recently, Large Language Models (LLMs) have made significant advancements and are now widely used across various domains. Unfortunately, there has been a rising concern that LLMs can be misused to generate harmful or malicious content. Though a line of research has focused on aligning LLMs with human values and preventing them from producing inappropriate content, such alignments are usually vulnerable and can be bypassed by alignment-breaking attacks via adversarially optimized or handcrafted jailbreaking prompts. In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks. RA-LLM can be directly constructed upon an existing aligned LLM with a robust alignment checking function, without requiring any expensive retraining or fine-tuning process of the original LLM. Furthermore, we also provide a theoretical analysis for RA-LLM to verify its effectiveness in defending against alignment-breaking attacks. Through real-world experiments on open-source large language models, we demonstrate that RA-LLM can successfully defend against both state-of-the-art adversarial prompts and popular handcrafted jailbreaking prompts by reducing their attack success rates from nearly 100% to around 10% or less.
Forward citations
Cited by 5 Pith papers
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Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.
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LLMs sometimes regain safe behavior after multi-turn jailbreaks, and this recovery can be measured with turn-level safety trajectories and metrics such as misalignment length and recovery duration.
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Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAS
MAST combines MCTS and DPO to train a policy that stealthily tampers with inter-agent messages, achieving high attack success and low detection across tasks, architectures, and LLMs.
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Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers
Summaries of LLM safety papers, paired with a completion-style payload containing a harmful query, jailbreak aligned LLMs at high reported success rates and expose a defense paper versus attack paper bias.
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Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks
CoopGuard's defer-tempt-analyze-coordinate agents cut reported jailbreak success and raise attacker token costs on the new EMRA benchmark, but the deceptive-rate metric is partly defined by the paper's own scoring rubric.
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