Last analyzed 9/9/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1123 evaluated models

Findings

985

Models evaluated

1123

Paper-evaluated models

Any selected model can match.

Refine by category

Findings match every selected category.

Attack Type1 selected

Attack Context

Architecture Components

Attack Surface

Impact2 selected

Advanced filters

Published fromPublished through

Research indexed from arXiv.

Evidence status

Filtered research findings

1 entry

Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Agent False Negative

Published 1/1/2026

Analyzed 2/21/2026

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…

Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering

Evaluated models: Claude Sonnet 4, DeepSeek Chat, GPT-5

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.