Large Language Models

What Makes a Good LLM Agent for Real-world Penetration Testing?

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

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

RSafe: Incentivizing Proactive Reasoning to Build Robust and Adaptive LLM Safeguards

NeurIPS 2025 work on adaptive reasoning-based safeguards for robust LLM safety moderation.

Dec 2, 2025

When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models

Revealing and analyzing text bias in Large Audio-Language Models when audio and text inputs disagree.

Nov 1, 2025

Oedipus: LLM-enchanced Reasoning CAPTCHA Solver

An LLM-enhanced framework demonstrating vulnerabilities in reasoning-based CAPTCHA systems through AI-powered solving.

Oct 1, 2025

IllusionCAPTCHA: A CAPTCHA based on Visual Illusion

A WWW 2025 CAPTCHA design that uses visual illusions to create human-friendly but AI-hard verification tasks.

Apr 28, 2025

Source Code Summarization in the Era of Large Language Models

A comprehensive study of source code summarization capabilities with Large Language Models.

Apr 27, 2025

Efficient Detection of Toxic Prompts in Large Language Models

ASE 2024 work on efficient greybox detection of toxic prompts for large language models.

Oct 27, 2024

GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models

CCS 2024 work on assessing and reducing gender bias in large language models.

Oct 14, 2024

PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing
PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing

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