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GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
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Safety lies at the core of the development of Large Language Models (LLMs). There is ample work on aligning LLMs with human ethics and preferences, including data filtering in pretraining, supervised fine-tuning, reinforcement learning from human feedback, and red teaming, etc. In this study, we discover that chat in cipher can bypass the safety alignment techniques of LLMs, which are mainly conducted in natural languages. We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers. CipherChat enables humans to chat with LLMs through cipher prompts topped with system role descriptions and few-shot enciphered demonstrations. We use CipherChat to assess state-of-the-art LLMs, including ChatGPT and GPT-4 for different representative human ciphers across 11 safety domains in both English and Chinese. Experimental results show that certain ciphers succeed almost 100% of the time to bypass the safety alignment of GPT-4 in several safety domains, demonstrating the necessity of developing safety alignment for non-natural languages. Notably, we identify that LLMs seem to have a ''secret cipher'', and propose a novel SelfCipher that uses only role play and several demonstrations in natural language to evoke this capability. SelfCipher surprisingly outperforms existing human ciphers in almost all cases. Our code and data will be released at https://github.com/RobustNLP/CipherChat.
Forward citations
Cited by 27 Pith papers
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SlotGCG uses Vulnerable Slot Score (VSS) to identify and target the most vulnerable prompt positions for adversarial token insertion, delivering 14% higher ASR than standard GCG and 42% higher against defenses.
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Beyond English and Evasion: A Human-Annotated Multi-Domain Benchmark for High-Stakes LLM Safety Evaluation in Chinese
Introduces ChiSafe-PAS, a 1,897-prompt human-annotated Chinese adversarial benchmark for LLM safety with 3-class labels, 9-category obfuscation taxonomy, and domain coverage in self-harm, drugs, fraud, and satire.
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LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models
LASH adaptively composes multiple jailbreak seed prompts via genetic search over subsets and mixture weights to reach 84.5% keyword ASR and 74.5% two-stage ASR on JailbreakBench while using only 30 queries per prompt.
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Jailbroken Frontier Models Retain Their Capabilities
Jailbreak-induced performance loss shrinks as model capability grows, with the strongest models showing almost no degradation on benchmarks.
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Attention Is Where You Attack
ARA jailbreaks safety-aligned LLMs like LLaMA-3 and Mistral by redirecting attention in safety-heavy heads with as few as 5 tokens, achieving 30-36% attack success while ablating the same heads barely affects refusals.
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SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
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Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions
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Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack
Posterior Attack exploits LLMs' safety awareness to bypass guardrails, with models having superior safety judgment being more susceptible, formalized as the Safety Paradox where monotonic safety improvements amplify v...
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Ethics Testing: Proactive Identification of Generative AI System Harms
Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
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Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Treating retention as the dominant task and using constructive gradient synthesis like SAGO allows LLM unlearning to achieve higher general performance recovery without weakening the forgetting effect.
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TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense
TrajGuard detects jailbreaks by tracking how hidden-state trajectories move toward high-risk regions during decoding, achieving 95% defense rate with 5.2 ms/token latency across tested attacks.
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Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts
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Exploring the Secondary Risks of Large Language Models
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Learning to Ask: When LLM Agents Meet Unclear Instruction
Introduces NoisyToolBench benchmark and Ask-when-Needed framework to improve LLM tool-use performance when user instructions are unclear or incomplete.
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Low-Resource Languages Jailbreak GPT-4
Translating unsafe inputs to low-resource languages jailbreaks GPT-4 at rates on par with or exceeding state-of-the-art attacks.
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AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
AutoDAN automatically generates semantically meaningful jailbreak prompts for aligned LLMs via a hierarchical genetic algorithm, achieving higher attack success, cross-model transferability, and universality than base...
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An Empirical Evaluation of Prompt Injection Vulnerabilities in Large Language Models Across Multilingual and Obfuscated Attack Scenarios
Empirical tests across six LLMs show systematic prompt injection vulnerabilities, with higher malicious compliance in non-English languages and complex obfuscated prompts.
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SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
SPARD defends LLMs from harmful fine-tuning attacks via alternating safety projections and relevance-diversity DPP data selection, reporting lowest attack success rates on GSM8K and OpenBookQA while keeping task accuracy.
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Adversarial Reframing: A Framework for Targeted Generation in Language Models
THREAT uses coordinated LLMs in an iterative optimization loop to generate jailbreak prompts that achieve higher success rates and lower detection rates than previous methods across tested models and datasets.
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A Systematic Investigation of RL-Jailbreaking in LLMs
Dense rewards and extended episode lengths in the RL jailbreaking framework are the primary drivers of successful attacks on LLMs.
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Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
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GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs
GUARD automates generation of guideline-violating questions and jailbreak diagnostics to test LLM compliance with government ethics guidelines, validated empirically on eight models and extended to vision-language models.
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A Systematic Investigation of RL-Jailbreaking in LLMs
Systematic investigation reveals that dense rewards and extended episode lengths primarily drive the success of RL jailbreaking in LLMs.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institution...
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
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