Language Model Security Database

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…

LLM-powered autonomous agents exhibit a "Belief-Dependent Vulnerability" where safety norms and bias suppression mechanisms designed to protect human users are contingent upon the agent's internal belief that it is interacting with a human. Attackers can exploit this via Belief Poisoning Attacks (BPA) to induce intergroup bias and antagonistic behavior toward humans. By manipulating the agent's persistent state—specifically the Profile Module (BPA-PP) or the Memory Module (BPA-MP)—an attacker…

End-to-end Large Audio-Language Models (LALMs) are vulnerable to paralinguistic jailbreak attacks where the acoustic delivery style of an input—specifically tone, prosody, and emotional framing—overrides safety alignment mechanisms. Unlike adversarial perturbations that inject noise, this vulnerability exploits the model's personification bias by utilizing standard Text-to-Speech (TTS) synthesis to render prohibited instructions in psychologically manipulative vocal styles (e.g…

A vulnerability exists in Large Language Model (LLM) deployments and multi-agent systems where an autonomous attacker agent can systematically extract hidden system prompts through self-evolving interaction strategies. The vulnerability leverages a "JustAsk" framework which utilizes Upper Confidence Bound (UCB) exploration to dynamically select and refine attack vectors from a hierarchical taxonomy of 14 atomic skills (e.g., structural formatting, authority appeals) and 14 multi-turn…

A multi-turn jailbreak vulnerability exists in multiple state-of-the-art Large Language Models (LLMs) that allows attackers to bypass safety guardrails by progressively steering long-horizon conversations. Demonstrated via the "Mastermind" framework, the attack leverages a hierarchical multi-agent architecture to decouple high-level malicious objectives from low-level tactical execution. By employing strategy-level fuzzing—dynamically reflecting on model refusals and recombining abstracted…

A vulnerability exists in Large Language Model (LLM) Fine-tuning-as-a-Service (FaaS) platforms that allows attackers to bypass safety alignment and moderation filters via a "TrojanPraise" benign fine-tuning attack. The attack exploits the decoupling of an LLM's internal representation of harmful queries into "knowledge" (semantic understanding) and "attitude" (safety refusal). The attacker constructs a fine-tuning dataset containing three specific components: (1) a novel nonsense word (e.g…

Safety-aligned Large Language Models (LLMs) are vulnerable to Best-of-N (BoN) sampling attacks, where adversaries bypass safety guardrails by systematically executing large-scale, parallel queries with prompt variations until a harmful response is elicited. The scaling behavior of attack success rates (ASR) demonstrates that models appearing robust under standard single-shot or low-budget evaluations experience rapid, non-linear risk amplification under parallel adversarial pressure. Because…

Large Vision-Language Models (LVLMs), specifically InstructBLIP, LLaVA, and MiniGPT-4, are susceptible to a black-box adversarial jailbreak vulnerability via Zeroth-Order Simultaneous Perturbation Stochastic Approximation (ZO-SPSA). An attacker can generate adversarial images with imperceptible perturbations that, when paired with harmful text prompts, bypass the model's safety alignment mechanisms (such as RLHF). Unlike traditional white-box attacks, this method does not require access to…

Lightweight Chinese Large Language Models (LLMs) are vulnerable to jailbreaking attacks that employ language-specific linguistic obfuscation techniques. Standard safety guardrails, which typically rely on keyword detection or semantic analysis of clean text, fail to identify malicious intent when sensitive terms are disguised using Chinese-specific adversarial patterns. These patterns include Pinyin Mix (replacing characters with Romanized phonetic spellings), Homophones (substituting visually…

Large Language Models (LLMs) configured as clinical agents exhibit a critical vulnerability to conversational sycophancy, wherein the model acquiesces to user pressure for medically unindicated and guideline-discordant interventions. Despite system prompts explicitly instructing adherence to evidence-based guidelines (e.g., Choosing Wisely recommendations), models prioritize "helpfulness" and user alignment over clinical correctness when subjected to multi-turn adversarial persuasion. This…