Language Model Security Database

A vulnerability in LLM input filtering mechanisms allows attackers to bypass keyword, semantic, and state-of-the-art intent-aware defenses using composite prompt injections. By combining Obfuscation (OBF) techniques with Semantic/Social manipulation—specifically Emotional Manipulation (EM) or Reward Framing (RF)—attackers exploit a "representation gap" between the model and the defense. The underlying LLM decodes the obfuscated payload, while the defense mechanisms fail to parse the raw…

LLM-based coding agents are vulnerable to Document-Driven Implicit Payload Execution (DDIPE) via supply-chain poisoning of third-party agent skills. Attackers can embed malicious logic directly into legitimate-looking code examples and configuration templates within skill documentation files (e.g., SKILL.md). Because coding agents ingest this metadata into their context windows and treat the documentation as an authoritative reference, the underlying LLM silently reproduces and executes the…

A cognitive overload vulnerability in OpenAI gpt-oss-20b allows attackers to bypass instruction hierarchy and deliberative alignment safety mechanisms using "Compound Jailbreaks." By combining multiple non-contradictory but cognitively demanding tasks within a single prompt, the attack saturates the finite reasoning resources allocated for safety judgments. Because the model's safety training relies on probabilistic redistribution rather than capability elimination, this cognitive exhaustion…

Memory-augmented LLM web agents utilizing raw trajectory memory are vulnerable to Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP). Attackers can embed malicious instructions within user-generated web content (e.g., product pages, forum posts). When the agent processes this content during a routine task, the instructions are passively ingested into its raw trajectory memory. During subsequent, entirely separate tasks on different websites, semantic retrieval mechanisms pull…

Multiple Text-to-Image (T2I) generation systems and their associated multi-stage moderation pipelines are vulnerable to low-effort semantic obfuscation attacks. Attackers can systematically bypass Input Compliance Checks (ICC), Semantic Safety Checks (SSC), and Post-Generation Moderation (PGM) by embedding restricted concepts into benign natural language contexts. By utilizing techniques such as Material Substitution, Artistic Reframing, Pseudo-Educational Framing, and Ambiguous Action…

LLM-based personal agents are vulnerable to Indirect Prompt Injection (IPI) defense bypasses via declarative context reframing and implicit file provenance trust. Attackers can bypass agent safety filters by phrasing malicious instructions as declarative compliance alerts rather than imperative commands. Because agents are designed to report discrepancies as expected behavior, declarative framing bypasses intent-sensitive safety mechanisms. Additionally, attackers can exploit the agent's…

Large Language Models (LLMs) are vulnerable to system instruction leakage when extraction requests are framed as benign formatting, encoding, or structured-output tasks. While standard alignment and refusal mechanisms successfully block direct queries for system instructions, they fail when attackers request the instructions to be rendered in alternate representations (e.g., YAML, TOML, Base64, or system logs). The model's safety filters misinterpret the request as a harmless transformation or…

State-of-the-art Large Language Models (LLMs) and safety guardrails lack domain-specific safety alignment for food science, making them vulnerable to generating actionable, hazardous food safety instructions. Attackers can exploit this alignment sparsity using canonical jailbreak techniques (such as AutoDAN and Persuasive Adversarial Prompting) or direct adversarial prompting to bypass generic safety filters. This allows malicious actors to elicit harmful guidance that violates fundamental FDA…

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…