
NimicStack
NimicStack is the pure Nim implementation of Call Stack Spoofing technique to mimic legitimate programs

NimicStack is the pure Nim implementation of Call Stack Spoofing technique to mimic legitimate programs

Crystal Palace library for proxying Nt API calls via the Threadpool. Updated for call gadgets.


Training-free language model modification: Remove censorship, suppress slop, reduce sycophancy, and more

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, DeepSeek, and…

Modular LLM vulnerability scanner that probes for hallucination, data leakage, prompt injection, jailbreaks, and toxicity using static, dynamic, and…

A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine…

Set of tools to assess and improve LLM security.

Open-source framework for red-teaming generative AI systems: automate attack prompts, score model responses, and audit behavior to identify security…

Empire is a post-exploitation and adversary emulation framework that is used to aid Red Teams and Penetration Testers.

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Source code about machine learning and security.

Interactive dashboards and libraries for responsible AI model debugging, covering error analysis, fairness, interpretability, counterfactuals, causal…

This repository contains detailed adversary simulation APT campaigns targeting various critical sectors. Each simulation includes custom tools, C2…

Real-time deepfake toolkit for penetration testing of identity verification and video conferencing systems. Supports face swap, image animation, and…

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in…

Improve your security and privacy by blocking ads, tracking and malware domains.

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX