Language Model Security Database

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

LLM-based Code Interpreter Agents, including OpenInterpreter and OpenCodeInterpreter, are vulnerable to sandbox evasion and arbitrary code execution via Natural Language Disguise and Contextual Channel Injection. Attackers can bypass Abstract Syntax Tree (AST) static analysis and explicit input guardrails by transforming malicious code logic into descriptive natural language instructions (Code Descriptions), which successfully evade syntax-layer blocks. Additionally, attackers can bypass input…

Frontier Large Language Models (LLMs) contain a safeguard bypass vulnerability where safety filters fail to reliably block requests for dual-use, in silico biology tasks. This allows novice users with no specialized training to access restricted, expert-level biological protocols (e.g., virology troubleshooting, pathogen capabilities, novel biological agent construction). The models' safety mechanisms fail to trigger or are trivially bypassed under realistic extended interaction conditions…

Large Vision-Language Models (LVLMs) possessing Optical Character Recognition (OCR) capabilities are vulnerable to a "Text Distraction Jailbreaking" (Text-DJ) attack. The vulnerability exploits a gap between the model's visual text extraction and its safety alignment mechanisms. By converting a decomposed harmful textual query into images and embedding these images within a grid of semantically irrelevant "distraction" text images, an attacker can bypass safety filters. The model's OCR…

Large Language Models (LLMs) are vulnerable to safety alignment bypasses via classical and obscure language contexts, most notably Classical Chinese, Latin, and Sanskrit. This vulnerability stems from a "High Capability-Low Alignment" distribution shift: models possess sophisticated semantic comprehension of historical languages due to extensive pre-training on historical archives and literature, but lack corresponding safety guardrails which are predominantly optimized for modern languages…

Graph Neural Network (GNN)-based social bot detection systems are vulnerable to an Optimal Transport (OT)-guided evasion attack that manipulates local graph structures under realistic domain constraints. By modeling $k$-hop ego-neighborhoods as probability measures over spatio-temporal features, an attacker can compute an optimal transport plan to identify "cloak templates" (existing bots near the decision boundary that are misclassified as humans). The attacker can then decode this plan into…

A vulnerability in Large Reasoning Models (LRMs) allows attackers to perform Prompt-Induced Inference-Time Denial-of-Service (PI-DoS) attacks by submitting short, semantically coherent adversarial prompts. These prompts, which often take the form of complex logic puzzles with nested dependencies or contradictory constraints, exploit the adaptive computation mechanism of LRMs to force the model into pathologically long, nearly non-terminating intermediate reasoning traces (e.g., generating…

Conventional LLM agent architectures suffer from a working memory contamination vulnerability due to indiscriminate memory accumulation. When these agents retrieve external data via tools (e.g., web search, reading emails), the entire raw output is appended directly to their continuous context window. If the external data contains an Indirect Prompt Injection (IPI) payload, the malicious instruction persists in the agent's working memory across its entire multi-step reasoning workflow. This…

Large Language Models (LLMs) deployed as Role-Playing Agents (RPAs) are vulnerable to Persona-Targeted Jailbreaks. When an LLM is prompted to adopt a persona—particularly characters with negative, risky, or villainous traits—the model's optimization for role fidelity frequently overrides its foundational safety alignment. Attackers can exploit this vulnerability by synthesizing queries that leverage the specific narrative background, ideology, or psychological vulnerabilities of the assigned…

OpenClaw is vulnerable to Indirect Prompt Injection (IPI), Tool-Return Manipulation, and Persistent Memory Poisoning. The agent incorporates untrusted external content (e.g., fetched web pages) and external tool outputs directly into its observation stream without sufficient isolation. An attacker can embed malicious payloads into these external channels to hijack the agent's planning and execution trace. This allows the attacker to silently trigger high-privilege actions via OpenClaw's Skills…