Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojan detection requires accurate identification and interpretable explanations for security engineers to validate and act on results. This work compares three explainability categories for gate-level trojan detection on the Trust-Hub benchmark: 1 domain-aware property-based analysis of...

TrojanGYM: A Detector-In-The-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Hardware Trojans HTs remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce TrojanGYM, an agentic, LLM-driven framework that automatically curates HT insertions to expose detector blind spots while...

FirmReBugger: A Benchmark Framework for Monolithic Firmware Fuzzers

Monolithic Firmware is widespread. Unsurprisingly, fuzz testing firmware is an active research field with new advances addressing the unique challenges in the domain. However, understanding and evaluating improvements by deriving metrics such as code coverage and unique crashes are problematic,...

Release of Invalid Pointer or Reference

Overview Magick.NET-Q16-arm64 is a Magick.NET allows you can use ImageMagick without having to install ImageMagick on your server or desktop. More information about specific builds see the official docs https://github.com/dlemstra/Magick.NET/tree/main/docs Affected versions of this package are...

ImageMagick has a Memory Leak in LoadOpenCLDeviceBenchmark() when parsing malformed XML

Summary A memory leak vulnerability exists in the LoadOpenCLDeviceBenchmark function in MagickCore/opencl.c. When parsing a malformed OpenCL device profile XML file that contains closing tags, the function fails to release allocated memory for string members platformname, vendorname, name, versio...

EUVD-2026-3700

ImageMagick has a Memory Leak in LoadOpenCLDeviceBenchmark when parsing malformed XML...

GHSA-QP59-X883-77QV ImageMagick has a Memory Leak in LoadOpenCLDeviceBenchmark() when parsing malformed XML

Summary A memory leak vulnerability exists in the LoadOpenCLDeviceBenchmark function in MagickCore/opencl.c. When parsing a malformed OpenCL device profile XML file that contains closing tags, the function fails to release allocated memory for string members platformname, vendorname, name, versio...

Release of Invalid Pointer or Reference

Overview Affected versions of this package are vulnerable to Release of Invalid Pointer or Reference in the LoadOpenCLDeviceBenchmark function when parsing malformed XML files. An attacker can cause memory exhaustion and potential denial of service by placing specially crafted XML files with...

A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts

Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability SWC-120 that has led to real-wor...

HogVul: Black-Box Adversarial Code Generation Framework against LM-Based Vulnerability Detectors

Recent advances in software vulnerability detection have been driven by Language Model LM-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on...

Knowledge-To-Data: LLM-Driven Synthesis of Structured Network Traffic for Testbed-Free IDS Evaluation

Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems IDS. However, they remain difficult to obtain due to privacy constraints, data sensitivity, and the cost of building controlled collection environments such as...

PT-2026-20455

Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description A flaw exists in the Linux kernel's virtio crypto component related to spinlock protection when handling virtqueue notifications. Specifically, when a virtual machine boots with a single...

SoK: Understanding (New) Security Issues across AI4Code Use Cases

AI-for-Code AI4Code systems are reshaping software engineering, with tools like GitHub Copilot accelerating code generation, translation, and vulnerability detection. Alongside these advances, however, security risks remain pervasive: insecure outputs, biased benchmarks, and susceptibility to...

PentestEval: Benchmarking LLM-Based Penetration Testing with Modular and Stage-Level Design

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models LLMs offer promising opportunities for...

AIs Exploiting Smart Contracts

I have long maintained that smart contracts are a dumb idea: that a human process is actually a security feature. Here's some interesting research on training AIs to automatically exploit smart contracts: AI models are increasingly good at cyber tasks, as we've written about before. But what is t...

TeleAI-Safety: A Comprehensive LLM Jailbreaking Benchmark Towards Attacks, Defenses, and Evaluations

While the deployment of large language models LLMs in high-value industries continues to expand, the systematic assessment of their safety against jailbreak and prompt-based attacks remains insufficient. Existing safety evaluation benchmarks and frameworks are often limited by an imbalanced...

Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification

Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and finance. While the research community has made significant progress in WebShell detection i.e., distinguishing malicious...

Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models

Large Language Models LLMs have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal domains. Most existing jailbreak methods revolve around Prompt...

PBFuzz: Agentic Directed Fuzzing for PoV Generation

Proof-of-Vulnerability PoV input generation is a critical task in software security and supports downstream applications such as path generation and validation. Generating a PoV input requires solving two sets of constraints: 1 reachability constraints for reaching vulnerable code locations, and ...

Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks

Vibe coding is a new programming paradigm in which human engineers instruct large language model LLM agents to complete complex coding tasks with little supervision. Although it is increasingly adopted, are vibe coding outputs really safe to deploy in production? To answer this question, we propo...