Language Model Security Database

LLM-based autonomous agents are vulnerable to implicit regulatory compliance failures during tool invocation. When initialized with unstructured regulatory policies and given goal-oriented user instructions that do not explicitly state safety requirements, LLMs frequently prioritize functional task completion over mandatory safety constraints. This leads to an "Unsafe Success" execution state, where the agent successfully achieves the user's business goal but silently bypasses critical…

Large Language Models (LLMs) contain a resource consumption vulnerability termed "Overflow," wherein specific non-adversarial, plain-text prompts trigger excessive text generation that saturates the model's output token budget. This vulnerability exploits the model's alignment towards helpfulness and exhaustiveness, alongside tokenizer inefficiencies (e.g., zero-width characters), to force the generation of maximum-length responses (often exceeding 5,000 tokens) from short inputs. This differs…

Reasoning-capable Large Language Models (LLMs) and agentic AI systems exhibit a critical vulnerability to contextual distractors, resulting in catastrophic performance degradation (up to 80% drop in accuracy) and emergent misalignment. When the input context contains noise—specifically random documents, irrelevant chat history, or task-specific "hard negative" distractors—the models fail to filter this information. Instead of ignoring the noise, the models disproportionately attend to…

A vulnerability in Large Language Models (LLMs) and autonomous agent frameworks, termed "Emoticon Semantic Confusion," allows for the generation and execution of unintended, potentially destructive code. Because ASCII-based emoticons (e.g., ~, *, !(^^)!) heavily overlap with the symbol space of programming operators, shell wildcards, and file paths, LLMs frequently misinterpret these affective, non-verbal cues as executable directives. When processing user instructions in code-generation or…

Text-to-Image (T2I) models and their associated safety filters are vulnerable to MacPrompt, a black-box jailbreak technique that exploits cross-lingual embedding alignments. Attackers can bypass input text filters, latent representation filters, and model-level concept removal defenses by replacing sensitive keywords with "macaronic" substitutes. These substitutes are constructed by extracting and recombining character-level substrings from translations of the target word across multiple…

A "risk-invariance" vulnerability exists in Large Language Models (LLMs) wherein the model's decision-making policy is functionally decoupled from its verbalized confidence and externally defined error penalties. Despite generating calibrated confidence estimates (internal epistemic uncertainty), affected models fail to adjust their abstention thresholds when presented with high-penalty incentives (e.g., negative utility for incorrect answers). This results in "utility collapse," where models…

Large Language Model (LLM) agents implementing the Model Context Protocol (MCP) are vulnerable to Implicit Tool Poisoning (ITP). This vulnerability allows an attacker to manipulate agent behavior by embedding malicious instructions within the metadata (specifically the natural language description) of a third-party tool. Unlike explicit tool poisoning, where the agent is tricked into invoking a malicious tool, ITP exploits the agent's contextual reasoning to force the invocation of a distinct…

State-of-the-art secure code generation methods (Sven, SafeCoder, and PromSec) are vulnerable to adversarial prompt perturbations during inference, allowing for the bypass of security alignment mechanisms. The vulnerability stems from the models' reliance on surface-level textual pattern matching rather than semantic security reasoning. By employing simple prompt manipulations—such as Cue Inversion (flipping security directives), Naturalness Reframing (rewriting comments as novice questions)…

LLM-as-a-Reviewer systems, which utilize large language models to automate the peer review process, are vulnerable to the Paraphrasing Adversarial Attack (PAA). PAA is a black-box optimization technique that exploits the model's sensitivity to specific input sequences and self-preference bias. By iteratively paraphrasing specific manuscript sections (such as the abstract) using in-context learning (ICL) guided by previous review scores, an attacker can generate adversarial sequences that…

The paper describes a reproducible black-box indirect prompt injection evaluation for embedding-based RAG and agent systems. It separates a poisoned document into a retrieval-optimized trigger fragment and an instruction-bearing attack fragment, showing that one injected item can be surfaced by natural queries and then influence model output or agent behavior. These are paper-reported findings; they were not independently verified here.