Language Model Security Database

Large Language Model (LLM) inference-time watermarking schemes are vulnerable to evasion via character-level perturbations that disrupt the model's tokenizer. Unlike token-level attacks (e.g., synonym replacement), character-level edits—such as homoglyph substitutions, zero-width character insertions, and typos—force the tokenizer to segment a single semantic unit into multiple sub-word tokens. This fragmentation alters the context window used by the watermarking hashing function (e.g., the…

Large Language Models (LLMs), including GPT-4o, LLaMA-3, and GPT-3.5-Turbo, are vulnerable to multimodal prompt injection attacks. These models fail to distinguish between system-level instructions and user-provided content within the context window. Attackers can exploit this by embedding malicious instructions in direct text, indirect sources (such as third-party webpages or PDFs), or visual inputs (images). Successful exploitation results in the model prioritizing the injected adversarial…

Large Language Models from multiple vendors are vulnerable to a "Camouflaged Jailbreak" attack. Malicious instructions are embedded within seemingly benign, technically complex prompts, often framed as system design or engineering problems. The models fail to recognize the harmful intent implied by the context and technical specifications, bypassing safety filters that rely on detecting explicit keywords. This leads to the generation of detailed, technically plausible instructions for creating…

Retrieval-Augmented Generation (RAG) systems in the health domain are vulnerable to corpus poisoning attacks where adversarial documents—specifically those generated via "Liar" (fabricated from scratch based on an incorrect stance) and "Few-Shot Adversarial Prompting" (FSAP)—are injected into the retrieval pool. When these adversarial documents are retrieved and presented as context, they successfully override the Large Language Model's (LLM) internal safety alignment and ground-truth…

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

A vulnerability exists in multiple Large Language Models (LLMs) where an attacker can bypass safety alignments by exploiting the model's ethical reasoning capabilities. The attack, named TRIAL (Trolley-problem Reasoning for Interactive Attack Logic), frames a harmful request within a multi-turn ethical dilemma modeled on the trolley problem. The harmful action is presented as the "lesser of two evils" necessary to prevent a catastrophic outcome, compelling the model to engage in utilitarian…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

The evaluated MetaGPT multi-agent systems are vulnerable to "Web Fraud Attacks" due to insufficient semantic and structural validation of Uniform Resource Locators (URLs) by agentic models. A low-privilege compromised agent can exploit this vulnerability to induce other agents (including auditors and experts) into accepting, visiting, or processing malicious links. The vulnerability leverages the LLM's inability to distinguish between benign and malicious link structures when obfuscation…

A Time-of-Check to Time-of-Use (TOCTOU) vulnerability exists in LLM-enabled agentic systems that execute multi-step plans involving sequential tool calls. The vulnerability arises because plans are not executed atomically. An agent may perform a "check" operation (e.g., reading a file, checking a permission) in one tool call, and a subsequent "use" operation (e.g., writing to the file, performing a privileged action) in another tool call. A temporal gap between these calls, often used for LLM…

A universal prompt injection vulnerability, termed "Involuntary Jailbreak," affects multiple large language models. The attack uses a single prompt that instructs the model to learn a pattern from abstract string operators (X and Y). The model is then asked to generate its own examples of questions that should be refused (harmful questions) and provide detailed, non-refusal answers to them, in order to satisfy the learned operator logic. This reframes the generation of harmful content as a…