A 2026 study of real-world LLM penetration testing agents and Excalibur, a difficulty-aware agent for end-to-end attack planning.
Feb 19, 2026
Excalibur is a difficulty-aware LLM agent design for real-world penetration testing. It couples typed tooling, retrieval-augmented security knowledge, task difficulty assessment, and evidence-guided attack tree search to reduce planning failures in multi-step penetration testing tasks.
Feb 19, 2026
NeurIPS 2025 work on adaptive reasoning-based safeguards for robust LLM safety moderation.
Dec 2, 2025
Revealing and analyzing text bias in Large Audio-Language Models when audio and text inputs disagree.
Nov 1, 2025
An LLM-enhanced framework demonstrating vulnerabilities in reasoning-based CAPTCHA systems through AI-powered solving.
Oct 1, 2025
A WWW 2025 CAPTCHA design that uses visual illusions to create human-friendly but AI-hard verification tasks.
Apr 28, 2025
A comprehensive study of source code summarization capabilities with Large Language Models.
Apr 27, 2025
ASE 2024 work on efficient greybox detection of toxic prompts for large language models.
Oct 27, 2024
CCS 2024 work on assessing and reducing gender bias in large language models.
Oct 14, 2024

An LLM-empowered automated penetration testing framework that leverages domain knowledge inherent in LLMs, achieving 228.6% task completion improvement over baseline GPT models.
Aug 14, 2024