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.

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

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

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

🛡️ 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.

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

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

ML-infrastructure-aware anomaly detection system for protecting model weights against exfiltration, using a 3-layer cascaded architecture (Rules → ML → LLM).

Enterprise-grade, distributed MLOps platform to simulate, detect, and mitigate Neural Trojans (backdoors) in DNNs—covering both offensive generation and defensive forensic audits with an end-to-end microservices architecture.

AI supply chain security scanner: detects ML-specific risks (model weight poisoning, dataset contamination, gradient-based backdoors) that traditional scanners miss. The Snyk for AI. govML-governed.

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

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.

Scan any Hugging Face repo for malicious signals before model download. Detects org impersonation, pickle exploits, and supply chain attacks.

Deterministic scanner that detects code-executing chat templates in model files (GGUF / Ollama / Hugging Face) before you load the model.

Master's thesis on undetectability of architectural and weight-manipulation backdoors in neural networks.