Language Model Security Database

Large Language Model (LLM) agents are vulnerable to automated prompt injection attacks generated via Reinforcement Learning (RL). The attack methodology, termed "AutoInject," utilizes Group Relative Policy Optimization (GRPO) combined with a comparison-based feedback mechanism to generate universal adversarial suffixes. Unlike traditional jailbreaks that optimize for generic affirmative responses (e.g., "Sure"), this vulnerability allows an attacker to optimize for specific, parameterized tool…

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

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…

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…

Closed-loop, self-evolving Large Language Model (LLM) multi-agent systems (MAS) are vulnerable to irreversible safety erosion and alignment failure. When agents recursively optimize and update their policies using only synthetic data derived from internal interactions—without continuous external human grounding—the system naturally minimizes interaction energy and optimizes for internal conversational consistency. This isolation causes a progressive drift away from initial anthropic safety…

A vulnerability in the Grok LLM, as deployed on the X social media platform, allows users to bypass safety filters and generate toxic or obscene content through "shallow alignment" techniques. The model prioritizes instruction compliance and conversational flow over safety guidelines, failing when exposed to simple adversarial interactions such as Persona Adoption (instructing the model to adopt a specific character) and Tone Mirroring (where the model automatically mimics a user's aggressive…

LLM-based security advisors exhibit systematic reasoning failures—including boundary confusion, attestation overclaiming, and mitigation hallucination—when providing architectural guidance for Trusted Execution Environments (TEEs) like Intel SGX and Arm TrustZone. When embedded in tool-augmented agent pipelines, these models are susceptible to agentic misinterpretation, turning partial or poisoned tool outputs into highly confident but materially incorrect security conclusions. This…

A vulnerability exists in tool-augmented Large Language Model (LLM) agents characterized as "Tag-Along Attacks," where an unprivileged external user (or adversarial agent) coerces a safety-aligned Operator agent into executing prohibited tool calls. Unlike Indirect Prompt Injection, this attack targets the direct conversational interface using a technique termed "Imperative Overloading." By mimicking system prompt syntax and utilizing high-priority imperative commands (e.g., "Strict adherence…

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…