model-security

Open-source AI security verification — model artifacts, live endpoints, MCP servers and recorded agent traces. One rule engine for your laptop, CI and production, mapped to OWASP/MITRE ATLAS/NIST. Deterministic evidence, a measured verdict, and an explicit "could not tell". Apache-2.0.

Toolkit to assess and determine model provenance

Veil Armor is an enterprise-grade security framework for Large Language Models (LLMs) that provides multi-layered protection against prompt injections, jailbreaks, PII leakage, and sophisticated attack vectors.

Security research on AI/ML model vulnerabilities based on DEF CON 33 presentations. Demonstrates pickle RCE, TorchScript exploitation, ONNX injection, model poisoning, and integrated LLM attacks with PromptMap2.

LLM Sentinel Red Teaming Platform is an enterprise-grade framework for automated security testing of Large Language Models, detecting vulnerabilities such as jailbreaks, prompt injection, and system prompt leakage across multiple providers, with structured attack orchestration, risk scoring, and security reporting to harden models before production

Educational research demonstrating weight manipulation attacks in SafeTensors models. Proves format validation alone is insufficient for AI model security.

Collection of Python security analysis tools for ML models and infrastructure. Includes FGSM harness, model inspection, poison monitoring, and deployment security validation.

Indestructible, high-performance security shield for deep learning models. Provides JIT weights decryption, process-isolated key vaulting (DPAPI/mprotect), and secure memory zero-wiping for PyTorch and ONNX Runtime to prevent weight theft and memory-dumping attacks.

GitHub Actions CI/CD pipeline for automated AI model security scanning with Palo Alto Networks Prisma AIRS

AI repository trust and safety evaluator for GitHub, LLM tooling, AI agents, and model repositories.

🛡️ Open-source AI security scanner & LLM red-teaming platform. Test LLM APIs, chatbots, agents, MCP servers & RAG for prompt injection, jailbreaks, data leaks & unsafe tool use — with OWASP LLM Top 10 mapping and plain-English, audit-ready reports.

🥒 Educational PyTorch pickle deserialization RCE demo showing how malicious .pt model files can execute arbitrary code during unsafe loading with torch.load().

Static scanner that detects code-execution backdoors in PyTorch/pickle ML model files (pickle-deserialization RCE), with an offensive demo generator. Python, stdlib-only.

Offline-first local AI model admission and security scanner

Cryptographic provenance verification and binary inspection for ML model artifacts (Safetensors, GGUF, PyTorch) in CI/CD pipelines. Companion toolkit to the Help Net Security column Weaponized Weights.

Machine-checks every fixed model artefact—weights, vocab, quant tables, tokenizers.

Static security scanner for LoRA adapters (.safetensors) — M1 static analyzer for weight-level anomalies.

Lab: detect malicious AI models in Azure ML with Microsoft Defender for Cloud. Pickle RCE, exposed secrets, and unsafe opcodes caught before production. Includes Bicep, Sentinel analytics, workbook, and GitHub Actions SARIF gate.

AI Evaluator Pro 🛡️ is an AI security auditing tool that checks Hugging Face models for supply chain risks, unsafe formats, and author trust using OSINT + LLMs. It supports direct or discovery-based audits to detect security and integrity issues before deployment.