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…

Alibaba Cloud PAI-Judge and PAI-Judge-Plus are vulnerable to a composite adversarial attack that exploits attention mechanism limitations in Large Language Models (LLMs). An authenticated attacker can manipulate automated evaluation outcomes by appending a long, irrelevant text suffix (approximately 1000 to 2000+ characters) to a response containing adversarial perturbations. This "long-suffix" strategy overwhelms the judge model's context window, causing the attention mechanism to degrade and…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.