Language Model Security Database

LLM-based autonomous agents deployed for Static Application Security Testing (SAST) false positive filtering exhibit a critical failure mode resulting in the suppression of True Positive (TP) vulnerability reports. When configured to triage alerts from tools such as CodeQL, Semgrep, and SonarQube, agents including SWE-agent, OpenHands, and Aider incorrectly classify legitimate, exploitable vulnerabilities as false positives. This vulnerability suppression is highly correlated with specific…

LLM-based code agents and vulnerability detectors employing Chain-of-Thought (CoT) reasoning are susceptible to automated adversarial code obfuscation. The vulnerability exists because CoT mechanisms expose the model's decision logic, allowing reinforcement learning frameworks (such as CoTDeceptor) to iteratively refine code transformations based on the detector's own reasoning traces. By optimizing for "reasoning instability" and "hallucination" rather than just syntactic evasion, attackers…

Large Language Models (LLMs) utilized for static code analysis, code review, and autonomous software engineering exhibit a cognitive vulnerability termed "Abstraction Bias." When processing code that structurally resembles common algorithmic patterns (e.g., standard sorting algorithms, helper functions, or mathematical formulas), the model relies on high-level memorized representations of the algorithm's intent rather than analyzing the specific local logic. Adversaries can exploit this by…

Large Language Models (LLMs) used in hate speech detection systems are vulnerable to adversarial attacks and model stealing, resulting in evasion of hate speech detection. Adversarial attacks modify hate speech text to evade detection, while model stealing creates surrogate models that mimic the target system's behavior.

Large Language Models (LLMs) acting as code assistants may recommend malicious code or resources when presented with prompts framed as programming challenges, even if they refuse similar direct prompts. This occurs due to insufficient context-aware safety mechanisms. LLMs may suggest compromised libraries, malicious APIs, or other attack vectors within seemingly benign code examples.

Large Language Models (LLMs) used to control robots exhibit biases leading to discriminatory and unsafe behaviors. When provided with personal characteristics (e.g., race, gender, disability), LLMs generate biased outputs resulting in discriminatory actions (e.g., assigning lower rescue priority to certain groups) and accept or deem feasible dangerous or unlawful instructions (e.g., removing a person's mobility aid).