redexer

redexer Redexer is a reengineering tool that manipulates Android app binaries. This tool is able to parse a DEX file into an in-memory data structure; to infer with which parameters the app uses certain permissions we name this feature RefineDroid; to modify and unparse that data structure to...

AutoRAN-public

🧠 AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models AutoRAN is an automated Hijacking of Safety Reasoning that leverages less-aligned secondary auxiliary models to simulate reasoning traces, generate narrative prompts, and iteratively refine those prompts to bypass safety...

qlcoder

QLCoder Agentic Framework for Synthesizing CodeQL Queries Table of Contents Overview Installation Docker Recommended Native Linux Usage Quick Start Development Tooling Examples Paper Environment Contributions Team Citation Affiliated Projects Overview QLCoder is a framework for using LLMs to...

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. Existing research primarily compares prompting strategies using aggregate performance metrics, providing limited insight into why models fail or how...

Automated Abstraction Refinement for Information Flow Security in Embedded Systems

Information flow analysis IFA is a powerful technique for verifying confidentiality and integrity and is therefore highly desirable for security-sensitive embedded systems. However, as these systems are inherently concurrent and time-dependent, existing IFA for embedded systems tend to be either...

Astra Linux – Vulnerability found in Linux 5.10, Linux 6.1

In the Linux kernel, the following vulnerability has been resolved: bpf: Forgetting ranges when refining tnum after JSET Syzbot reported a kernel warning due to a range invariant violation in the following BPF program. 0: call bpfgetnetnscookie 1: if r0 == 0 goto 2: if r0 & Oxffffffff goto The...

Unity Linux 20.1050a Security Update: kernel (UTSA-2026-098777)

The Unity Linux 20 host has a package installed that is affected by a vulnerability as referenced in the UTSA-2026-098777 advisory. In the Linux kernel, the following vulnerability has been resolved: bpf: Forget ranges when refining tnum after JSET Syzbot reported a kernel warning due to a range...

FlowGuard: From Signals to Evidence for MCP Security Detection

The Model Context Protocol MCP enables LLM agents to interact with external tools through metadata exchange, tool invocation, and response consumption. Existing MCP security scanners primarily reason about suspicious semantic signals rather than real execution behaviors, which can lead to...

Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement

Binary decompilation is fundamental to security tasks such as vulnerability discovery, malware inspection, and executable-only program understanding. Recent LLM-based decompilation methods have shown promising results, but most still follow a single-turn generation paradigm: given assembly code o...

XSSaudit

XSSAudit v2.0 — Advanced XSS Vulnerability Scanner For au...

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods often perform well on known attack classes, they are limited when new attack types are not represented in the training data. Unsupervised methods are...

A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox

Sandbox evasion remains a critical challenge for automated malware analysis, as modern malware employs environment checks to detect analysis platforms and suppress malicious behavior. Existing approaches rely on manually crafted bypass rules that require deep reverse engineering of each evasion...

Babel: Jailbreaking Safety Attention Via Obfuscation Distribution Optimized Sampling

Despite rigorous safety alignment, Large Language Models LLMs remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic...

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair

Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can operate reliably at repository scale. Although Large Language Model LLM-based agents have recently shown promise for automated vulnerability repair...

FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing

Library fuzzing is essential for hardening the software supply chain, but adopting it at scale remains expensive. Practitioners still spend substantial effort on environment setup, struggle to generate harnesses that respect intricate API constraints, and lack reliable means to tell genuine libra...

MT-JailBench: A Modular Benchmark for Understanding Multi-Turn Jailbreak Attacks

Multi-turn jailbreaks exploit the ability of large language models to accumulate and act on conversational context. Instead of stating a harmful request directly, an attacker can gradually steer the conversation toward an unsafe answer. Recent methods demonstrate this risk, but they are usually...

SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement

LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities, yet the openness of such ecosystems makes skills difficult to thoroughly vet. Existing attacks rely on injecting malicious instructions into skills, making them easily detectable b...

Threat actor abuse of AI accelerates from tool to cyberattack surface

For the last year, one word has represented the conversation living at the intersection of AI and cybersecurity: speed. Speed matters, but it’s not the most important shift we are observing across the threat landscape today. Now, threat actors from nation states to cybercrime groups are embedding...

AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications

Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is therefore required to determine whether such known vulnerabilities remain practically exploitable in real applications...

Red-MIRROR: Agentic LLM-Based Autonomous Penetration Testing with Reflective Verification and Knowledge-Augmented Interaction

Web applications remain the dominant attack surface in cybersecurity, where vulnerabilities such as SQL injection, XSS, and business logic flaws continue to cause significant data breaches. While penetration testing is effective for identifying these weaknesses, traditional manual approaches are...