Open-source alternatives to Ai Exploits

This project is no longer actively maintained. You are welcome to fork and continue its development on your own. Thank you for your interest and support.

Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples, incorporate them into training loops, and benchmark the resilience of machine learning models. It provides a gradient-based attack framework for constructing both white-box and black-box attacks to identify model misclassifications. The project includes capabilities for model robustness benchmarking, allowing users to evaluate and verify how models resist evasion attacks and malicious input perturbations. It also facilitates adversarial training to increase a model's resistance to pert

PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model applications. It functions as a generative AI red teaming framework used to simulate adversarial attacks and identify weaknesses in system guardrails. The tool automates AI risk assessment by scanning generative AI components for security vulnerabilities. It utilizes automated testing and analysis to identify security gaps and prevent potential exploits through a consistent, repeatable process. The system incorporates asynchronous model orchestration to compare security postures

Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th

A curation of awesome tools, documents and projects about LLM Security.

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Play with ChatGPT-Web and found the HTML rendering in description settings.

OpenAI CLIP text encoders for any language

The official implementation of Task Residual for Tuning Vision-Language Models (accepted to CVPR 2023).

Borg cluster traces from Google

We present a new class of vulnerabilities and impacts stemming from "indirect prompt injection" affecting language models integrated with applications. Our demos currently span GPT-4 (Bing and synthetic apps) using ChatML, GPT-3 & LangChain based apps in addition to proof-of-concepts for attacks…

A curated list of useful resources that cover Offensive AI.

Shannon is an integrated security platform designed for autonomous penetration testing, static and dynamic analysis, and automated vulnerability remediation within self-hosted, private infrastructure. It functions as a unified security suite that orchestrates the entire lifecycle of vulnerability management, from initial discovery and reachability prioritization to the generation and verification of code-level patches. The platform distinguishes itself through its agentic approach to security, deploying autonomous agents to execute both black-box and white-box exploits against running applica

This repo is the official implementation of "CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet".

Overview Requirements Docker Installing from scratch Data VOA training data Evaluation data Code Preprocessing Text IE scripts Text OpenIE scripts Vision IE scripts IE visualizations Negative description sample generation Training Configuration file Training Testing

Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users

[Paper](https://arxiv.org/abs/2210.05663) [Website](https://mahis.life/clip-fields/) [Code](https://github.com/notmahi/clip-fields) [Data](https://osf.io/famgv) [Video](https://youtu.be/bKu7GvRiSQU)

Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language model conversational systems. It functions as security middleware that intercepts inputs and outputs to block prompt injections, jailbreaks, and sensitive data leaks, while providing a conversational dialogue manager to define structured interaction flows through configuration files. The framework includes a hallucination filter to screen model outputs for factual accuracy and a specialized modeling language for defining conversational flows and constraints. It provides capabiliti

Jiahao Li, Greg Shakhnarovich, Raymond A. Yeh Toyota Technological Institute at Chicago (TTIC)