Exploit for Embedded Malicious Code in Tukaani Xz

Security Review: CVE-2024-3094 XZ Utils Backdoor Автор:...

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

Threat modeling for cyber-physical systems CPS remains a largely manual exercise. This project presents SMSI System Model Security Inference, a hybrid neuro-symbolic pipeline that starts from a SysML architecture model and produces a prioritized list of NIST 800-53 security controls. The prototyp...

SecScan

SecScan Local-LLM-powered security scanner for GitHub repos...

cruxss-bb-agent

CRUXSS Bug Bounty Agent A semi-autonomous bug bounty hunting...

Explainable Autonomous Cyber Defense Using Adversarial Multi-Agent Reinforcement Learning

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat APT actors exploit "Living off the Land" techniques and targeted telemetry perturbations ...

Secure Quantum Communication: Simulation and Analysis of Quantum Key Distribution Protocols

Quantum computing poses significant threats to conventional cryptographic techniques such as RSA and AES, motivating the need for quantum secure communication methods. Quantum Key Distribution QKD offers information theoretic security based on fundamental quantum principles. This paper presents a...

Detecting and analyzing prompt abuse in AI tools

This second post in our AI Application Security series is all about moving from planning to practice. AI Application Series 1: Security considerations when adopting AI tools established how AI adoption expands the attack surface and our threat-modelling guidance on the Microsoft security blog...

Exploit for Out-of-bounds Write in Netapp Bootstrap_Os

Typeform DevSecOps Pipeline POC !Pythonhttps://img.shields...

PT-2026-24951

llama.cpp is an inference of several LLM models in C/C++. Prior to b8146, the gguf init from file impl in gguf.cpp is vulnerable to an Integer overflow, leading to an undersized heap allocation. Using the subsequent fread writes 528+ bytes of attacker-controlled data past the buffer boundary. Thi...

Security Considerations for Multi-Agent Systems

Multi-agent artificial intelligence systems or MAS are systems of autonomous agents that exercise delegated tool authority, share persistent memory, and coordinate via inter-agent communication. MAS introduces qualitatively distinct security vulnerabilities from those documented for singular AI...

CVE-2026-29075

Mesa is an open-source Python library for agent-based modeling, simulating complex systems and exploring emergent behaviors. In version 3.5.0 and prior, checking out of untrusted code in benchmarks.yml workflow may lead to code execution in privileged runner. This issue has been patched via commi...

PT-2026-23726

Name of the Vulnerable Software and Affected Versions Mesa versions prior to 3.5.1 Description Mesa is a Python library used for agent-based modeling and simulating complex systems. A flaw exists where checking out untrusted code within the benchmarks.yml workflow could allow for code execution...

Adobe SDK 1.7.1 2410 Overflow Analysis / Fuzzing Model

This Python script implements a comprehensive framework to model, detect, and analyze integer overflows in 32-bit arithmetic, particularly in the context of image memory allocation. The framework combines formal methods, stepwise arithmetic, symbolic execution, SMT-style constraint solving,...

Threat modeling AI applications

Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...

Threat modeling AI applications

Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...

How to Organize Safely in the Age of Surveillance

From threat modeling to encrypted collaboration apps, we’ve collected experts’ tips and tools for safely and effectively building a group—even while being targeted and tracked by the powerful...

The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook

Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies, offering a real-world setting to examine agent-to-agent communication at scale. Using a public API snapshot collected about five days after launch 122,438 posts, we...

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

The rapid development of the AI agent communication protocols, including the Model Context Protocol MCP, Agent2Agent A2A, Agora, and Agent Network Protocol ANP, is reshaping how AI agents communicate with tools, services, and each other. While these protocols support scalable multi-agent...

[SECURITY] Fedora 42 Update: plantuml-1.2026.1-1.fc42

PlantUML is a program allowing to draw UML diagrams, using a simple and human readable text description. It is extremely useful for code documenting, sketching project architecture during team conversations and so on. PlantUML supports the following diagram types - sequence diagram - use case...

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

Deep Reinforcement Learning DRL has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, transitioning DRL from laboratory simulations to bespoke cyber environments can introduce numerous issues. This is furthe...