PT-2026-31562

Open AI models detect vulnerabilities by analyzing code, configs, and system behavior step-by-step—like spotting the FreeBSD zero-day CVE-2024-47467 in the Mythos showcase. Even a 3B open model nailed it, matching bigger ones. The chart shows rankings flip across tasks: OWASP easy stuff vs. gnarl...

An Agentic Multi-Agent Architecture for Cybersecurity Risk Management

Getting a real cybersecurity risk assessment for a small organization is expensive -- a NIST CSF-aligned engagement runs $15,000 on the low end, takes weeks, and depends on practitioners who are genuinely scarce. Most small companies skip it entirely. We built a six-agent AI system where each age...

Penetration Testing of Agentic AI: A Comparative Security Analysis across Models and Frameworks

Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks demonstrated that ChatGPT-4o successfully executes attacks as an agent that it refuses in chat mode, there is no comparative analysis in multiple...

An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection

Insider threats are a growing organizational problem due to the complexity of identifying their technical and behavioral elements. A large research body is dedicated to the study of insider threats from technological, psychological, and educational perspectives. However, research in this domain h...

JsDeObsBench: Measuring and Benchmarking LLMs for JavaScript Deobfuscation

Deobfuscating JavaScript JS code poses a significant challenge in web security, particularly as obfuscation techniques are frequently used to conceal malicious activities within scripts. While Large Language Models LLMs have recently shown promise in automating the deobfuscation process,...

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets

In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise AWGN into the real-world In-phase and...

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability

Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the...

Bootiful Spring Boot 3.4: Spring AI

I love Spring AI. It’s an amazing project designed to bring the patterns and practices of AI engineering to the Spring Boot developer. It’s got clean idiomatic abstractions that’ll make any Sring developer feel right at home, and it has a ton of integrations with all manner of different vector...