colabcat

Colabcat Run Hashcat on Google Colab with session restore capabilities with Google Drive. Usage Go to the link below to open a copy of the colabcat.ipynb file in Google Colab: https://colab.research.google.com/github/someshkar/colabcat/blob/master/colabcat.ipynb Click on Runtime, Change runtime...

penglab

Penglab Abuse of Google Colab for fun and profit. 🐧 What is it ? Penglab is a ready-to-install setup on Google Colab for cracking hashes with an incredible power, really useful for CTFs. See benchmarks below. It installs by default : Hashcat John Hydra SSH with ngrok And now, it can also : Launch...

cloudsploit

CloudSploit by Aqua - Cloud Security Scans Quick Start Generic $ git clone https://github.com/aquasecurity/cloudsploit.git $ cd cloudsploit $ npm install $ ./index.js -h Docker $ git clone https://github.com/aquasecurity/cloudsploit.git $ cd cloudsploit $ docker build . -t cloudsploit:0.0.1 $...

harbian-audit

harbian-audit Hardening Introduction Hardened Debian GNU/Linux and CentOS 8 distro auditing. The main test environment is in debian GNU/Linux 9/10/11/12/13 and CentOS 8 and ubuntu 22, and other versions are not fully tested. There are no implementations of desktop related items in this release. T...

DbDat

DbDat Db Database Assessment Tool DbDat performs numerous checks on a database to evaluate security. The categories of checks performed are configuration, privileges, users, and information. Checks are performed by running queries or reading database configuration files. The goal of this tool is ...

Adhrit

Adhrit is an open source Android APK reversing and analysis suite. The tool is an effort to find an efficient solution to all the needs of mobile security testing and automation. Adhrit has been built with a focus on flexibility and mudularization. Adhrit currently uses the Ghera benchmarks to...

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...

dockle

Dockle - Linter de imágenes de contenedor para seguridad, ayudando a construir la imagen Docker de mejores prácticas, fácil de empezar Dockle te ayuda a: 1. Construir imágenes Docker de Mejores Prácticas 2. Construir imágenes Docker seguras Los puntos de control incluyen CIS Benchmarksbash $ brew...

csf

ArmourBird CSF - Marco de Seguridad para Contenedores Nota: El Cliente CSF está en desarrollo activo y se está convirtiendo a GoLang para un mejor rendimiento y arquitectura Tabla de Contenidos 1. Acerca de 2. Diagrama de Arquitectura 3. APIs del Servidor CSF 4. Instalación/Uso 5. Construcción de...

Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks

Security benchmarks for LLM-based agents often report the attack success rate ASR as a measure of model robustness and use these scores to compare different models and defense mechanisms, assuming that they describe the security of the agent. In this paper, we explore whether it also influences t...

Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks

Large language model LLM benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines. We audit eight cybersecurity benchmarks across 10 proprietary, open-weight, and cybersecurity-specialized LLMs. By modeling benchmarks as...

io.zipkin:benchmarks (2.22.1) potentially affected by CVE-2026-82265 via io.zipkin:zipkin-server (2.22.1)

io.zipkin:zipkin-server MAVEN version 2.22.1 is affected by a known vulnerability. The following packages have a transitive dependency on io.zipkin:zipkin-server and may be impacted: - io.zipkin:benchmarks 2.22.1 Source cves: CVE-2026-82265 Source advisory: SNYK:JAVA-IOZIPKIN-19477049...

Evaluating and Preventing Security Smells in AI-Generated Ansible Code

AI coding assistants generate Infrastructure as Code, yet no work has examined whether this code meets security requirements. This matters because security smells in infrastructure code propagate to deployed systems, producing infrastructure that is insecure and untrustworthy. We evaluate 16 AI...

CLEAR: Causal Context-Based Agentic Reasoning for Vulnerability Detection

Detecting source code vulnerabilities is increasingly difficult as modern security flaws are rooted in complex causal dependencies between execution flows, control conditions, and program states. Despite recent advances in Large Language Models LLMs and multi-agent frameworks, existing approaches...

AI Security Leaderboard: Methodology, Results and Minimal Standard

Frontier AI model developers increasingly rely on layered safeguards to prevent catastrophic misuse, but little public evidence exists on how much protection these safeguards provide, or how consistently across developers. We introduce the FAR.AI Minimal Standard for Safeguards, Version 1.0: a...

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

Provenance-based intrusion detection systems PIDS frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols. We investigate this dependency by re-evaluating representative PIDS on public datasets tha...

CoGate: Confidence-Gated Co-Decoding for Secure Code Generation

Large language models are widely used for code generation, but they can also produce insecure programs due to patterns learned from their pretraining data. Decoding-time steering has become an important solution to this problem: a small expert model is combined with the target model at each step ...

From Backlog Items to Security Guidance: Towards Continuous Security Compliance

Continuous software engineering in regulated domains requires engineering teams to address security throughout the development lifecycle. Yet making security requirements explicit in backlog items is still problematic. Engineers must instead infer security relevance of backlog items from brief,...

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

Large language model LLM agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows defenders to inject deceptive observations that can mislead the agent's decision-making process. However, existing...

What AI Red-Team Evaluations Can and Cannot Prove

Red-team evaluations of AI models support some claims and not others, and the boundary between the two is calculable rather than merely a matter of judgment. We define the evidential ceiling of an evaluation as the largest factor by which one result can move belief under a fixed testing budget,...