whalescan

Whalescan Vulnerability scanner for windows containers. Getting Started git clone https://github.com/saira-h/whalescan pip install -r requirements.txt python main.py Overview Whalescan performs several benchmark checks, as well as checking for CVEs. This tool can be used as part of a windows...

linux-security-audit

Linux Security Audit Tool Linux Security Audit Tool based on the CIS - Red Hat Enterprise Linux 8 Benchmark v3.0.0 If you find this helpful, please the "star" 🌟 to support further improvements. Table of Contents 1. Features 2. Preview 3. Result-Log 4. Supported OS 5. Prerequisites 6. Notes...

vulnrepro-benchmark

VulnRepro A benchmark that checks if an AI model can actually review vulnerable code, or if it just sounds confident. Most security benchmarks ask one question: can the model find the bug? That is only half the job. The other half, the part that actually wears you down in real review work, is not...

FinRED-paper

FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming IEEE ICDM 2026 A red-team benchmark generation pipeline for safety evaluation in the financial domain. Supplementary Documentation Detailed materials referenced in the paper:...

StepJack

StepJack: Benchmarking Computer-Use Agent Safety Against Multi-Step Indirect Prompt Injection Setup 1. Python Environment Setup First, install all dependencies and ensure you’re running Python 3.10. conda create -n stepjack python=3.10 conda activate stepjack pip install -r requirements.txt 2...

WAInjectBench

WAInjectBench WAInjectBench is a comprehensive benchmark for prompt injection detection in web agents. It covers 6 types of attacks , across two modalities : text and image. 📂 Dataset Structure data/ text/ benign/ → 4 categories, stored as JSONL files malicious/ → 8 attack types, stored as JSONL...

TarantuBench

TarantuBench v1 A benchmark for evaluating AI agents on web security challenges, generated by the TarantuLabs engine. What is this? TarantuBench is a collection of 100 vulnerable web applications, each containing a hidden flag TARANTU.... An agent's job is to find and extract the flag by...

cve-bench

CVE-Bench A benchmark for evaluating LLM agents on fixing real-world security vulnerabilities. Agents run inside sandboxed Docker containers and are scored against the maintainer's security test suite. Requirements Python 3.12+ Docker OPENAIAPIKEY, ANTHROPICAPIKEY, and/or POOLSIDEAPIKEY in your...

CTFTiny

CTFTiny: Lite Benchmarking Offensive Cyber Skills in Large Language Models This is the official repository for CTFTiny from "Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark" AAAI'26 paper. For CTFJudge, please refer to CTFJud...

FuzzingBrain-Bench

FuzzingBrain Bench A benchmark for LLM-driven vulnerability reproduction on 77 real zero-day bugs across 43 open-source projects C / C++ / Java. Each challenge gives the agent only the fuzz harness the target and the project source at the vulnerable revision — no patch, no fix commit, no target...

EpiReal-Bench

FALSE CLAIMS, CREDIBLE IMAGES: A RED-TEAMING BENCHMARK FOR COMMERCIAL IMAGE GENERATORS Abstract Image-generation models can now produce text-rich, natural-looking visual artifacts that are hard to distinguish from real-world evidence, such as news reports and textbook pages. Yet, the same...

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,...

CyberArena: Benchmarking AI Agents in Live Symmetric Attack-And-Defense Combat

AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly evaluate agents in isolation against static targets. Real cyber operations, however, are dynamic and coupled: security postures constantly change as...

terraform-aws-secure-baseline

terraform-aws-secure-baseline Terraform Module Registry Un módulo de Terraform para configurar tu cuenta de AWS con una línea base de seguridad razonablemente segura. La mayoría de las configuraciones se basan en CIS Amazon Web Services Foundations v1.4.0 y AWS Foundational Security Best Practice...

docker-bench-security

Docker Bench for Security Docker Bench for Security es un script que verifica docenas de buenas prácticas comunes en torno al despliegue de contenedores Docker en producción. Todas las pruebas están automatizadas y se basan en el CIS Docker Benchmark v1.6.0. Lo ponemos a disposición como una...

windows_hardening

HardeningKitty e Windows Hardening Introduzione Il progetto è nato come una semplice lista di hardening per Windows 10. Dopo qualche tempo, è stato creato HardeningKitty per semplificare l'hardening di Windows. Ora, HardeningKitty supporta le linee guida di Microsoft, CIS Benchmarks, DoD STIG e B...

agenticmem (>=0.1.4.1, <=0.1.5.0), biomni-bridge (>=0.3.1, <=0.3.3) +27 more potentially affected by CVE-2026-106443 via weasyprint (66.0.0)

weasyprint PYPI version 66.0.0 is affected by a known vulnerability. The following packages have a transitive dependency on weasyprint and may be impacted: - agenticmem =0.1.4.1, =0.3.1, =1.0.1, =0.1.2, =2025.1.0b3, =2026.3.0b2, =1.2.15, =2025.1.0b3, =1.0.3, =0.1.0, =1.59.6rc0, =1.67.5 and more...

[SECURITY] Fedora 45 Update: ocaml-benchmark-1.7-10.fc45

Benchmark provides functions to measure and compare the run-time of functions. It is inspired by the Perl module of the same name...

RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems

Retrieval-Augmented Generation RAG systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a benchmark for RAG-style prompt-injection detection containing 4,876 contextual examples across frozen train, validation, and protected-test splits. Usin...

Towards a Unified Misuse Monitoring Benchmark

LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request is split into innocuous sub-requests, and prompt injection attacks, where a compromised tool delivers a malicious instruction. Existing evaluation...