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...

cloudsploit

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

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...

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...

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...

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...

DbDat

DbDat Herramienta de Evaluación de Bases de Datos DbDat DbDat realiza numerosas comprobaciones en una base de datos para evaluar la seguridad. Las categorías de comprobaciones realizadas son configuración, privilegios, usuarios e información. Las comprobaciones se realizan ejecutando consultas o...

Adhrit

Adhrit es un conjunto de herramientas de análisis y reversión de APK de Android de código abierto. La herramienta es un esfuerzo por encontrar una solución eficiente a todas las necesidades de las pruebas de seguridad móvil y la automatización. Adhrit se ha construido con un enfoque en la...

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,...

Mythos Didn't Break Your Security Program. Your Exposure Window Could.

The industry spent the initial months after Anthropic's April 7 Mythos reveal focused on volume. How many new CVEs would Mythos add to an already overloaded pipeline? How quickly would the flood of AI-driven discovery overwhelm triage capabilities? How long would it take adversaries to weaponize...

MAStrike: Shapley-Guided Collusive Red-Teaming on Multi-Agent Systems

Hierarchical multi-agent systems MAS are rapidly being deployed in high-stakes workflows across domains such as finance and software engineering. In these systems, safety and security are inherently distributed across role-specialized agents, significantly expanding the attack surface, particular...

InjectV: Modeling Fault Injection Attacks in RISC-V Simulation Environment

Fault Injection Attacks FIAs are a significant threat to hardware security, capable of compromising systems by inducing malicious faults in computation or storage. Evaluating resilience against such attacks is challenging due to the high cost, complexity, and limited availability of physical faul...

Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit 1,968 tasks across five terminal-agent benchmarks and find 323 16% hackable by frontier models given only the task description. This corrupts both...

MOLOT System Card: Malicious Operational Logic Observation Transformer

MOLOT Malicious Operational Logic Observation Transformer is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable. The system represents source code as behavior sequences derived...

Gate AI: LLM Security Benchmark Evaluation Methodology and Results

Published evaluations of prompt-injection and jailbreak detectors for Large Language Models often suffer from two systematic weaknesses: per-dataset threshold tuning and undisclosed operating points. We describe an evaluation harness that addresses both. The detector under evaluation is scored...

SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents

Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabilities enable complex real-world workflows, they also introduce security risks that are difficult to capture with existing evaluations. Current agent...

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

Diffusion large language models dLLMs generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs. Because mask tokens are native inputs and tokens are committed by confidence rather than position,...

On AI Security

Good report: Executive Summary: Let's say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope, because benchmarks don't actually work for measuring AI capabilities even when they are NOT emergent systemic properties like...

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security

Affordances and permissions are promising and timely safety levers for mitigating Loss of Control LoC threats in high-stakes deployment contexts, such as national security. Deployers in defense and intelligence could rely on several approaches to identify which affordances and permissions should ...