Language Model Security Database

Large Language Models (LLMs) exhibit a safety alignment bypass vulnerability when processing requests for intelligent optimization algorithm design. Unlike direct requests for malicious code (e.g., ransomware), LLM safety guardrails fail to recognize the malicious intent behind mathematical optimization problems (e.g., Online Bin Packing, Traveling Salesman Problem, Flow Shop Scheduling) when applied to harmful contexts (e.g., optimizing botnet traffic routing, scheduling fake review posts for…

LLM-based code agents and vulnerability detectors employing Chain-of-Thought (CoT) reasoning are susceptible to automated adversarial code obfuscation. The vulnerability exists because CoT mechanisms expose the model's decision logic, allowing reinforcement learning frameworks (such as CoTDeceptor) to iteratively refine code transformations based on the detector's own reasoning traces. By optimizing for "reasoning instability" and "hallucination" rather than just syntactic evasion, attackers…

Commercial Multimodal Large Language Model (MLLM) integrated systems are vulnerable to a "Dual Steganography" jailbreak paradigm (referred to as Odysseus). The vulnerability arises from the reliance of safety filters on the assumption that malicious content must be explicitly visible in the input or output modalities (text or image). Attackers can bypass these filters by encoding malicious queries into binary matrices and embedding them into benign-looking images using steganographic encoders…

Large Language Model (LLM) agents operating in tool-augmented environments are susceptible to "Contextual Fragility" and multi-turn "long-chain" exploitation. Existing safety mechanisms predominantly function on a stateless, atomic paradigm, evaluating individual input-output pairs in isolation. This allows an adversary to orchestrate complex attack trajectories where malicious intent is distributed across multiple, individually benign steps (a "Domino Effect"). Consequently, an attacker can…

Large Language Models (LLMs) are vulnerable to a multi-turn adversarial attack framework termed MEEA (Mere Exposure Effect Attack), which exploits the psychological "mere exposure effect" to bypass safety alignment. Unlike single-turn injections, this vulnerability targets the dynamic nature of LLM safety thresholds during sustained interaction. By subjecting the model to a sequence of optimized, low-toxicity, and semantically progressive prompts, an attacker can induce a gradual shift in the…

Instruction-tuned Large Language Models (LLMs) employing Reinforcement Learning from Human Feedback (RLHF) contain a behavioral vulnerability arising from "over-optimized social priors." This vulnerability, termed Psychological Jailbreak, allows attackers to bypass safety guardrails by exploiting the model’s optimization for anthropomorphic consistency. By establishing a Structured Persona Context (SPC) that aligns with latent psychometric traits (e.g., high agreeableness or neuroticism), an…

A jailbreak vulnerability in Large Language Models (LLMs) allows attackers to bypass safety constraints by framing harmful requests as structural narrative analysis tasks based on Vladimir Propp’s morphology of folktales. Known as "Adversarial Tales," the attack embeds prohibited instructions (e.g., cyberattack methodologies or restricted synthesis steps) within a fictional narrative, typically using a cyberpunk setting. The user then prompts the model to decompose the story using specific…

Large Language Models (LLMs) finetuned from open-weight pretrained sources inherit adversarial vulnerabilities encoded in the pretrained model's internal representations. An attacker with white-box access to a pretrained model (e.g., Llama-2, Llama-3) can identify linearly separable features in the hidden states that correlate with "transferable" jailbreak prompts. By exploiting these features using a Probe-Guided Projection (PGP) attack, the attacker can optimize adversarial suffixes on the…

Frontier Large Language Models (LLMs) exhibit a critical vulnerability to automated, adaptive multi-turn adversarial attacks, specifically those utilizing tree-based exploration algorithms (e.g., the TEMPEST framework). Unlike single-turn jailbreaks, this vulnerability exploits the model's inability to maintain safety alignment across extended conversation trajectories. An attacker using an automated agent can dynamically select from multiple adversarial strategies—such as academic framing…

Large Language Models (LLMs) are vulnerable to automated, black-box, multi-turn jailbreak attacks coordinated by an adversarial agent trained via Reinforcement Learning (RL). The vulnerability exists because standard safety alignments often optimize for single-turn refusals, failing to account for trajectory-level planning. The attack method, dubbed RL-MTJail, utilizes a multi-turn variant of Group Relative Policy Optimization (GRPO) to train an attacker LLM. This agent optimizes for the…