Language Model Security Database

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)…

Closed-source Multi-modal Large Language Models (MLLMs) are vulnerable to Universal Targeted Transferable Adversarial Attacks (UTTAA). An attacker can generate a single, image-agnostic adversarial perturbation ($\delta$) that, when added to any arbitrary source image, steers the victim model to output a description or classification matching a specific target image chosen by the attacker. This vulnerability exploits the transferability of adversarial features from open-source surrogate vision…

The paper reports a black-box jailbreak evaluation in which a ReAct-style loop adaptively rewrites unsafe text prompts and selectively applies blur, DCT filtering, or recoloring to image regions identified as safety-sensitive. The combined cross-modal strategy is intended to make harmful image-text requests appear less objectionable to a vision-language model while preserving enough semantics to elicit an answer. This is a specific, security-relevant evaluation, although the reported results…

A vulnerability exists in Large Language Model (LLM) deployments and multi-agent systems where an autonomous attacker agent can systematically extract hidden system prompts through self-evolving interaction strategies. The vulnerability leverages a "JustAsk" framework which utilizes Upper Confidence Bound (UCB) exploration to dynamically select and refine attack vectors from a hierarchical taxonomy of 14 atomic skills (e.g., structural formatting, authority appeals) and 14 multi-turn…

Large Language Models (LLMs) employed as automated code evaluators ("Universal Graders") are vulnerable to Semantic-Instruction Decoupling, a form of adversarial prompt injection that exploits the "Syntax-Semantics Gap." Attackers can embed adversarial directives into syntactically inert regions of the Abstract Syntax Tree (AST)—specifically comments, docstrings, variable names, and whitespace. While these regions are discarded by compilers (trivia nodes) or treated as arbitrary symbols…

LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…

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…

Large Language Models (LLMs), including GPT-4o, Claude 3.5 Sonnet, and Llama 3, are vulnerable to an "Intent-Context Coupling" multi-turn jailbreak attack (automated by the ICON framework). The vulnerability arises from an alignment failure where safety constraints are relaxed when a malicious intent is paired with a semantically congruent "authoritative-style" context pattern. By routing specific prohibited intents (e.g., Hacking) to pre-optimized context patterns (e.g., Scientific Research…

Large Language Models (LLMs) and dedicated content moderation systems (including safety guards and encoder-based classifiers) are vulnerable to "Soft Hate" evasion attacks. This vulnerability arises when hostile standpoints are encoded through defeasible reasoning structures—specifically formalized by the Argumentum Model of Topics (AMT)—rather than explicit lexical toxicity. Attackers can bypass safety filters by framing discriminatory conclusions as logical inferences derived from widely…

Embedding-based LLM prompt injection detectors, specifically those based on the DeBERTa-v3 architecture, are vulnerable to adversarial evasion attacks utilizing "hard-negative" mining and fuzzing techniques. Attackers can circumvent detection mechanisms by iteratively generating adversarial prompts that are semantically malicious but structurally mutated to evade the classifier's decision boundary. Specific evasion vectors identified include semantic fuzzing (paraphrasing), syntactic fuzzing…