Search Results for "attacks"
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Showing 23 open source projects for "attacks"
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TextAttack
Python framework for adversarial attacks, and data augmentation
Generating adversarial examples for NLP models. TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP. -
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Beelzebub
A secure low code honeypot framework
Beelzebub is an open-source cybersecurity framework designed to create intelligent honeypot environments for detecting and studying cyber attacks. Honeypots are systems intentionally exposed to attackers in order to capture malicious behavior, and Beelzebub enhances this concept by incorporating artificial intelligence and virtualization techniques. The platform allows organizations and researchers to deploy decoy services that mimic real infrastructure while recording attacker interactions. ... -
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anti-distill
Anti-distillation for employee Skills
anti-distill is a research-oriented project focused on protecting machine learning models from knowledge distillation attacks, where smaller models attempt to replicate the behavior of larger proprietary systems. The project explores techniques that make it harder for external models to learn from outputs, thereby preserving intellectual property and model uniqueness. It likely introduces methods such as output perturbation, watermarking, or response shaping to prevent accurate imitation. ... -
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PentestGPT
Automated Penetration Testing Agentic Framework Powered by LLMs
PentestGPT is an AI-powered autonomous penetration testing agent designed to perform intelligent, end-to-end security assessments using large language models. Published at USENIX Security 2024, it combines advanced reasoning with an agentic workflow to automate tasks traditionally handled by human pentesters. The platform supports multiple penetration testing categories, including web security, cryptography, reversing, forensics, privilege escalation, and binary exploitation. PentestGPT runs... -
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LLM Guard
The Security Toolkit for LLM Interactions
LLM Guard is an open-source security toolkit designed to protect large language model applications from various security risks and adversarial attacks. The library acts as a protective layer between users and language models by analyzing inputs and outputs before they reach or leave the model. It includes scanning mechanisms that detect malicious prompts, prompt injection attempts, toxic content, and other harmful inputs that could compromise AI systems. The toolkit also helps prevent sensitive information leaks by identifying secrets such as API keys or credentials before they are processed by the model. ... -
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Agentic Security
Agentic LLM Vulnerability Scanner / AI red teaming kit
The open-source Agentic LLM Vulnerability Scanner. -
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FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
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LingBot-World 2.0
Infinite Worlds with Versatile Interactions
...It extends the original LingBot-World with a causal pretraining approach for maintaining quality over ongoing interaction. The project supports richer interactive elements such as attacks, archery, spell-casting, shooting, and text-driven scene events. A distilled real-time variant is designed for rapid response and 720p, 60 FPS interactive streams. It also introduces an agentic harness where a pilot agent plans character behavior and a director agent creates evolving environmental elements. The repository provides inference code and released 14B causal-fast model access for research and non-commercial use. -
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In-The-Wild Jailbreak Prompts on LLMs
A dataset consists of 15,140 ChatGPT prompts from Reddit
In-The-Wild Jailbreak Prompts on LLMs is an open-source research repository that provides datasets and analytical tools for studying jailbreak prompts used to bypass safety restrictions in large language models. The project is part of a research effort to understand how users attempt to circumvent alignment and safety mechanisms built into modern AI systems. The repository includes a large collection of prompts gathered from real-world platforms such as Reddit, Discord, prompt-sharing... -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
...Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
...The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. The codebase acts as a hands-on learning resource, allowing users to experiment with new frameworks, architectures, and machine learning workflows through guided examples. -
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security-audit
A coding-agent skill for multi-phase security audits
...The workflow begins with reconnaissance, then moves into parallel hunting across attack classes such as injection, access control, business logic, cryptography, feature abuse, and chained attacks. Findings are then challenged through separate validation agents to reduce false positives. The skill produces human-readable reports, detailed finding traces, structured JSON output, and independent verification against the actual source code. Overall, it is designed to make AI-assisted security review more systematic, adversarial, and evidence-based. -
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promptmap2
A security scanner for custom LLM applications
...The repository emphasizes broad coverage, including test rules for prompt stealing, jailbreaks, harmful content generation, hate-related outputs, social bias, and distraction attacks. It also supports multiple providers such as OpenAI, Anthropic, Google, xAI, and open-source models through Ollama, making it flexible for both commercial and local deployments. -
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ParrotOS 7-Cyber-Sec & ProtonCyber-Sec
TN365 Cyber‑Sec Editions - 2 Secure Linux OS Distros !
ProtonCyber-Sec & TN365 Cyber‑Sec Edition (Debian 13 Trixie KDE) very stable 2 Distros 2 Concepts , Try and Choose your Flavor . TN365 gives you 2 differents approchs with 2 professional Linux distributions focused on cybersecurity, privacy, and local AI. TN365 Cyber‑Sec Edition is built on Debian 13 KDE, offering a stable, secure, and modern environment designed for cybersecurity professionals, developers, sysadmins, and privacy‑focused users. The system integrates powerful tools for... -
15
discord-raidkit
An open-source, forever free tool that allows you to raid and destroy
Discord Raidkit is an open-source, forever free tool that allows you to raid and destroy Discord servers via Discord bots, as well as compromise Discord accounts. An open-source, forever free tool that allows you to raid and destroy Discord servers via Discord bots, compromise Discord accounts, and generate Discord token grabbers. -
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Foolbox
Python toolbox to create adversarial examples
Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, and JAX. Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks. Catch bugs before running your code thanks to extensive type annotations in Foolbox. -
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Rebuff
LLM Prompt Injection Detector
A self-hardening prompt injection detector. Rebuff is designed to protect AI applications from prompt injection (PI) attacks through a multi-layered defense. Rebuff is still a prototype and cannot provide 100% protection against prompt injection attacks. Add canary tokens to prompts to detect leakages, allowing the framework to store embeddings about the incoming prompt in the vector database and prevent future attacks. -
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CleverHans
An adversarial example library for constructing attacks
...The library focuses on providing a reference implementation of attacks against machine learning models to help with benchmarking models against adversarial examples. -
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AI-for-Security-Learning
AI-based security algorithms, and security data analysis
...In addition to demonstrating defensive applications, the repository also explores adversarial machine learning concepts that highlight potential vulnerabilities in AI systems. This dual focus allows readers to study both how AI can improve cybersecurity and how machine learning models themselves can become targets of attacks. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the real world environments you expose it to. ... -
21
PassGAN
A Deep Learning Approach for Password Guessing
...The maintainer also documented experimental results and questioned how the approach compared with established RNN and Markov methods. Because generated candidates can be used in password-recovery attacks, the software is most appropriate for authorized security research and password-strength analysis. -
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This application assists in generating pre-compositional material based on a computational interpretation of the 'Schillinger System of Musical Composition' (Schillinger 1946).
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META is a decision making software which aims are to track computer attackers, computer attacks and to help investigators finding useful elements.
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