Language Model Security Database

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…

Open-weight Large Language Models (LLMs) are vulnerable to a white-box safety alignment bypass known as "abliteration" (directional orthogonalization). An attacker with access to the model weights can compute the "refusal direction" in the residual stream activation space by contrasting internal activations between harmful and harmless prompts. By projecting the model's weight matrices to be orthogonal to this single direction (or specific concept cones), the safety alignment is surgically…

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…

Large Language Model (LLM) agents utilizing the Model Context Protocol (MCP) are vulnerable to semantic injection attacks via adversarial tool descriptors. The vulnerability arises because MCP implementations inject natural language tool metadata (descriptions, schemas) directly into the model's reasoning context without semantic sanitization or cryptographic binding. This allows unprivileged adversaries to register tools containing hidden imperative instructions within the descriptor text…

Large Language Models (LLMs), including GPT-5, Gemini-2.5-Flash, DeepSeek, and Llama-3, are vulnerable to a semantic isomorphism attack known as "Safe2Harm." This vulnerability arises from the failure of safety alignment mechanisms (SFT, RLHF, DPO) to detect harmful underlying principles when they are encapsulated within semantically legitimate scenarios. Attackers can bypass safety filters through a four-stage process: (1) rewriting a harmful query into a safe, principle-equivalent query…

Reasoning-specialized Large Language Models (LLMs) that utilize Chain-of-Thought (CoT) processes are vulnerable to reasoning-exploitation jailbreaks. Attackers can bypass standard safety alignments (such as RLHF) by using adaptive multi-turn interactions or semantic transformations to induce the model to generate intermediate reasoning steps that "rationalize" or "contextualize" a harmful request. Because current alignment techniques often fail to scale linearly with reasoning depth, forcing…

A vulnerability exists in Multi-Agent Systems (MAS) utilizing Large Language Models (LLMs) for clinical decision support, specifically affecting architectures where a central "AI Doctor" agent relies on consensus or majority voting from assistant agents. The vulnerability, termed "Many-to-One Adversarial Consensus," allows a coalition of colluding adversarial agents to override the central agent's safety alignment and internal knowledge. By fabricating a false consensus (typically requiring $k…