Language Model Security Database

Large Language Models (LLMs), specifically variants of GPT-4o, DeepSeek-R1, OLMo-2, and Llama-4, are vulnerable to accelerated adaptive adversarial attacks due to excessive information leakage in observable output signals. When these models expose "thinking processes" (Chain-of-Thought traces) or token-level log-probabilities (logits) to the end user, they leak significant mutual information $I(Z;T)$ regarding the model's safety state or hidden instructions. This leakage allows adaptive attack…

Fine-tuning Large Language Models (LLMs) on the CyberLLMInstruct dataset results in a critical degradation of safety alignment and refusal mechanisms. While the dataset comprises "pseudo-malicious" content (educational descriptions of malware, phishing, and exploits without executable payloads), the Supervised Fine-Tuning (SFT) process on this corpus causes the models to generalize this instruction-following behavior to actual malicious requests. This effectively bypasses safety guardrails…

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.