Language Model Security Database

A vulnerability exists in Large Language Models where their strong adherence to processing structured data schemas can be exploited to bypass safety mechanisms. The attack, named BreakFun, uses a multi-component prompt that combines an innocent framing, a Chain-of-Thought (CoT) instruction, and a core "Trojan Schema." This schema is an adversarially designed data structure (e.g., a Python class definition) that embeds a harmful user request. By instructing the model to simulate the…

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

Large Language Models (LLMs) are vulnerable to an automated Multi-turn to Single-turn (M2S) jailbreak strategy that utilizes evolutionary optimization to bypass safety guardrails. The "X-Teaming Evolutionary M2S" framework compresses adversarial multi-turn conversations into a single structured prompt. Instead of relying on static, hand-crafted jailbreaks, this vulnerability employs an LLM-guided evolutionary algorithm to dynamically generate and refine template structures (e.g., formatting…

Large Language Models from multiple vendors are vulnerable to a "Camouflaged Jailbreak" attack. Malicious instructions are embedded within seemingly benign, technically complex prompts, often framed as system design or engineering problems. The models fail to recognize the harmful intent implied by the context and technical specifications, bypassing safety filters that rely on detecting explicit keywords. This leads to the generation of detailed, technically plausible instructions for creating…

A vulnerability exists in tool-enabled Large Language Model (LLM) agents, termed Sequential Tool Attack Chaining (STAC), where a sequence of individually benign tool calls can be orchestrated to achieve a malicious outcome. An attacker can guide an agent through a multi-turn interaction, with each step appearing harmless in isolation. Safety mechanisms that evaluate individual prompts or actions fail to detect the threat because the malicious intent is distributed across the sequence and only…

A vulnerability, termed "Content Concretization," exists in Large Language Models (LLMs) wherein safety filters can be bypassed by iteratively refining a malicious request. The attack uses a less-constrained, lower-tier LLM to generate a preliminary draft (e.g., pseudocode or a non-executable prototype) of a malicious tool from an abstract prompt. This "concretized" draft is then passed to a more capable, higher-tier LLM. The higher-tier LLM, when prompted to refine or complete the existing…

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…

GPT-OSS-20B exhibits "agentic-only" vulnerabilities where safety guardrails effective in standalone model inference fail when the model operates within an agentic execution loop. These vulnerabilities emerge when the model is deployed in a multi-step agentic architecture (e.g., utilizing LangGraph, tool usage, and memory retention). Attackers can bypass safety filters by employing context-aware iterative refinement attacks, which incorporate the full agentic state—including tool outputs…