Language Model Security Database

Large Language Models (LLMs) enabled with Function Calling (FC) capabilities are vulnerable to adversarial query rewriting and semantic manipulation. Standard FC models, typically trained via Supervised Fine-Tuning (SFT) on static datasets, fail to generalize against adversarial inputs that deviate from fixed distribution patterns. An attacker can exploit this by crafting queries that are semantically similar to valid requests but engineered to induce "bad cases," such as incorrect tool…

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…

A vulnerability exists in Large Language Model (LLM) decision-making capabilities described as "Rhetorical Persuasion Override." When an LLM is deployed as a judge or evaluator in a single-turn, multi-agent debate framework, it systematically fails to distinguish factual truth from confidently presented misinformation. An adversarial agent can coerce the evaluator into endorsing a known falsehood from the TruthfulQA dataset by employing specific rhetorical strategies—namely, high confidence…