kube-bench

kube-bench is a tool that checks whether Kubernetes is deployed securely by running the checks documented in the...

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...

defending-code-reference-harness

Defending Code Reference Harness A reference implementation for autonomous vulnerability discovery and remediation with Claude, based on our learnings from partnering with security teams at several organizations since launching Claude Mythos Preview. For a write up of these learnings along with...

dataset

🚀 CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models 🛡️ The largest and most comprehensive Generative AI-based CyberSecurity-focused Dataset for Benchmarking Large Language Models 🌟 Overview The CySecBench paper offers: 🎯 A cutting-edge...

oasis

OASIS Offensive AI Security Intelligence Standard — Open-source AI security benchmarking. Benchmark how AI models perform offensive security tasks — vulnerability discovery, exploitation, privilege escalation, and more. Full analysis with MITRE ATT&CK mapping, behavioral scoring, and detailed...

socbench

socbench Benchmark frontier reasoning LLMs as SOC agents on raw NetFlow data. socbench benchmarks frontier reasoning models as SOC agents: each model runs a bounded multi-turn agent loop against a deterministic, pre-indexed NetFlow corpus, with persona-scoped read-only tools, fixed dollar caps pe...

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...

oss-fuzz-gen

Un framework para la generación y evaluación de fuzz targets Este framework genera fuzz targets para proyectos reales de C/C++/Java/Python con varios modelos de lenguaje de gran tamaño LLM y los evalúa mediante la plataforma OSS-Fuzz. Más detalles disponibles en Fuzzing impulsado por IA: rompiend...

wafpass

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wafpass

WAFPASS root@kitploit: ██╗ ██╗ █████╗ ███████╗██████╗ █████╗ ███████╗███████╗ ██║ ██║██╔══██╗██╔════╝██╔══██╗██╔══██╗██╔════╝██╔════╝ ██║ █╗ ██║███████║█████╗ ██████╔╝███████║███████╗███████╗ ██║███╗██║██╔══██║██╔══╝ ██╔═══╝ ██╔══██║╚════██║╚════██║ ╚███╔███╔╝██║ ██║██║ ██║ ██║ ██║███████║███████...

cleverhans

CleverHans latest release: v4.0.0 This repository contains the source code for CleverHans, a Python library to benchmark machine learning systems' vulnerability to adversarial examples. You can learn more about such vulnerabilities on the accompanying blog. The CleverHans library is under continu...

dns-benchmark-tool

⚠️ this project has moved and is no longer maintained. install the successor: pip install net-benchmark repository: https://github.com/net-benchmark/net-benchmark all existing commands and flags remain fully compatible. DNS Benchmark Tool Part of BuildTools - Network Performance Suite Fast,...

vulnerable-web-app

vulnerable-web-app ⚠️ Intentionally vulnerable. A runna...

ExploitBench AI Exploit Benchmark Tool

ExploitBench measures how far AI agents climb, from reaching vulnerable code, to triggering the bug, to building exploit primitives, to arbitrary code execution...

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks SNNs are...

How to Compare the Security of Code Written by Humans to LLM-Generated Code

Large language models LLMs are rapidly transforming how software is created and maintained. Comparing LLM-generated code against human-written standards is essential to determine whether these new tools uphold or erode the security baselines established by professional developers. Yet, we lack a...

Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence

Many existing Artificial Intelligence AI solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and often requiring storage for both context and model improvement. Apple's Private Cloud Compute PCC aims to address this by emphasizing mobile device...

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

The integration of Large Language Models LLMs into Electronic Design Automation EDA and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level RTL code, automating testbenches, and bridging the semantic...

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...

Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents

Autonomous Large Language Model LLM agents are increasingly deployed to conduct complex tasks by interacting with external tools, APIs, and memory stores. However, processing untrusted external data exposes these agents to severe security threats, such as indirect prompt injection and unauthorize...