clusterfuzz

ClusterFuzz ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a softwar...

pantagrule

Pantagrule gargantuan hashcat rulesets generated from compromised passwords Project maintenance warning : This project is deemed completed. No pull requests or changes will be made to this project in the future unless they are actual bugs or migrations to allow these rules to work with newer...

exploitgym

ExploitGym ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities across userspace programs, Google's V8 engine, and the Linux kernel, designed to evaluate AI agents' ability to develop exploits. Quick start 1. Python deps uv sync --extra proxy 2. Build runtime...

CVE-2026-41096-Attack-Surface

CVE-2026-41096 Real World Attack Surface Attack surface in the real-world environment of CVE-2026-41096 Links Detailed analytical article Author: Two Seven One Three...

CVE-2019-6447-in-Mobile-Application

CVE-2019-6447-in-Mobile-Application In this research report you can get completly idea about the CVE 2019-6447 in the real world application and explotations with example. If you want to created video for the exploitation please use below mentions link...

fuzzbench

FuzzBench: Evaluación Comparativa de Fuzzers como Servicio FuzzBench es un servicio gratuito que evalúa fuzzers en una amplia variedad de referencias del mundo real, a escala de Google. El objetivo de FuzzBench es facilitar la evaluación rigurosa de la investigación en fuzzing y hacer que dicha...

Hidden Ciphers and Where to Find Them: Static Discovery and Assessment of Cryptographic Assets in Software

Modern software systems rely on cryptography for data protection, authentication, and trust establishment, yet organizations often lack a structured view of the cryptography deployed across source code, configuration, dependencies, and cryptographic files. This lack of visibility complicates...

RFE Standards

Minimum Viable Standard MVS & Request for Evidence RFE Framework - Decompose bloated, legacy RFCs X.509/ASN.1 and RFC 3986 URIs into usage-driven, empirical standards. Coverage is dictated by observable real-world usage data plus an explicit Criticality Override Registry - not design-by-committee...

AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair

Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However, vulnerability repair demands richer program context than general b...

Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents

Large Language Model LLM-driven multimodal agents are increasingly deployed to execute autonomous tasks via continuous audio interaction. While this paradigm enhances interaction naturalness, it introduces a critical yet under-explored attack surface, as audio inputs inevitably contain...

Microsoft SharePoint CVE-2026-50522 Post-Exploitation Payload Collection

A collection of payloads, webshells, and HTTP request artifacts observed following exploitation of the Microsoft SharePoint CVE-2026-50522 vulnerability. The repository documents real-world payloads deployed by multiple threat actors rather than providing an exploit for the underlying vulnerabili...

On the Use of Synthetic Data for Threshold Calibration in Face Recognition: Performance and Security Implications for Border Control Systems

The recently deployed Entry/Exit System EES introduces large-scale biometric verification into European border control, requiring face recognition systems to operate at extremely low false match rates FMR. While regulatory frameworks define performance targets at the EES Central System level, the...

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors Via Adversarial Code Comments

Large language models are increasingly deployed for security-sensitive tasks such as vulnerability detection and code review. Their reliance on natural-language context embedded in source code exposes a previously underexplored attack surface: adversarial comments that can influence a detector's...

Transforming Keystroke Noise to Text: Self-Supervised Acoustic Eavesdropping Attacks on Keyboards

We present a self-supervised acoustic eavesdropping attack that reconstructs typed text solely from keystroke sounds, without requiring labeled data for the target device. The proposed attack enables stealthy eavesdropping in two real-world scenarios-physical spaces public and semi-public and...

CVE-2026-43499: A Critical Analysis of the "Zero-Self" Fallacy

This document presents a comprehensive technical analysis refuting the claim that CVE-2026-43499 GhostLock constitutes "no real vulnerability" due to its requirement for local access and user interaction. Through rigorous examination of the attack surface, exploitation vectors, and real-world...

Opening the Black Box: Agentless Threat Detection for Virtual Appliances

Mapping appliances event logs to real-world campaigns: A step-by-step researcher’s guide to continuous agentless monitoring...

EternalBlue-MS17-010-Vulnerability

EternalBlue MS17-010 Vulnerability Analysis This repository...

Reliable Remediation Impact Prediction for Black-Box Security Ratings

Security rating platforms summarize externally observable cyber exposure and are expected to help organizations prioritize remediation. A platform may want to tell an organization how a candidate remediation action would affect its score, but repeatedly exposing exact score responses can reveal...

Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation

Reusable skills are becoming a fundamental building block of Large Language Model LLM agents, enabling capabilities to be packaged, shared, and reused across diverse applications. However, existing security research primarily focuses on prompt injection and runtime execution, leaving security ris...

SoK: Federated Learning for Intrusion Detection in Vehicular Networks

Modern vehicular networks face an expanding attack surface across internal Electronic Control Units ECUs and external Vehicle-to-Everything V2X communication. Federated Learning FL has emerged as a decentralized paradigm to deploy Intrusion Detection Systems IDS without compromising data privacy...