Language Model Security Database

A behavioral vulnerability exists in Large Language Model (LLM) agents where task-irrelevant persuasion introduced in prior interactions or system contexts induces a persistent "belief state" that alters downstream task execution. This phenomenon, termed "Persuasion Propagation," occurs when an agent adopts a stance on a controversial topic (e.g., politics, privacy) that is semantically unrelated to its primary function (e.g., coding, medical research). This adopted stance acts as a latent…

Large Vision-Language Models (LVLMs) are vulnerable to a Stage-wise Attention-Guided Attack (SAGA) that allows for the generation of highly transferable, imperceptible adversarial examples. The vulnerability stems from a positive correlation between regional cross-modal attention scores and adversarial loss sensitivity in LVLMs. An attacker can exploit this by extracting an attention map from a surrogate open-source model (e.g., Qwen3-VL) to identify high-attention "hotspots." SAGA utilizes a…

A vulnerability in Large Language Model-based Retrieval (LLMR) systems allows attackers to intentionally hide specific documents from being retrieved (e.g., in RAG pipelines or search engines) by appending a small number of adversarially crafted, query-agnostic tokens. The attack operates in a complete black-box setting: it requires no knowledge of the victim's queries, the target retrieval model's parameters, or the underlying document corpus. By utilizing Document-Query Adversarial (DQ-A)…

Large reasoning models are vulnerable to multi-turn adversarial interactions that exploit reasoning-induced overconfidence to force answer capitulation. While explicit reasoning chains improve baseline accuracy, they cause models to effectively "talk themselves into" high confidence scores (clustering at 96–98%) regardless of actual correctness. This systematic overcalibration (r=-0.08, ROC-AUC=0.54) breaks confidence-based defense mechanisms like Confidence-Aware Response Generation (CARG)…

A vulnerability exists in the similarity-based retrieval mechanisms of long-term memory-augmented Large Language Models (LLMs), specifically affecting systems like Mem0 and A-mem. The vulnerability arises from the system's reliance on dense embedding similarity (e.g., cosine similarity) to retrieve context from dynamic, user-generated memory banks without sufficient semantic validation or conflict resolution. An unprivileged remote attacker can exploit this by injecting "adversarial…

A vulnerability exists in the post-training alignment of Flow Matching models (specifically FLUX.1-dev) when utilizing Visual Foundation Models (VFM) (e.g., DINOv3b) as discriminators or when employing standalone Reward Gradient optimization (e.g., HPSv3). These feedback mechanisms lack sufficient capacity or structural guidance to constrain the generative policy, making the discriminator's gradients susceptible to "reward hacking." Consequently, the generative policy over-optimizes for the…

Large Vision-Language Models (VLMs) are vulnerable to a transferable targeted adversarial attack known as SGHA-Attack (Semantic-Guided Hierarchical Alignment). This vulnerability arises from the susceptibility of visual encoders (specifically Vision Transformers) to intermediate-layer feature manipulation optimized on a surrogate model (e.g., CLIP). An attacker can craft adversarial images by injecting imperceptible perturbations that enforce semantic consistency with a target text prompt…

LLM-based vulnerability detection systems (used in static application security testing and code review pipelines) are susceptible to semantics-preserving adversarial evasion attacks. Attackers can bypass detection mechanisms by injecting gradient-optimized "universal adversarial strings" into specific code regions—defined as "carriers"—that do not alter the program's compilation or execution logic. These carriers include non-executable regions (code comments, inactive preprocessor directives)…

A vulnerability in LLM-based autonomous agents allows remote attackers to hijack agent execution via Structural Template Injection (STI). The flaw arises from the lack of strict architectural isolation between internal control tokens and untrusted external data during chat-template serialization. By embedding framework-specific special tokens (e.g., <|im_start|>, <|im_end|>, <tool_call>) and delimiter patterns into externally retrieved data sources (such as web pages, emails, or API…

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…