OpenAI Atlas

Agentic AI Systems Under Siege: Prompt Injections, Data Poisoning, and Tool Exploits

Independent expert Andrey Biryukov details how AI agents with file access, email capabilities, and API calls become high-value targets for attackers. The article examines confidentiality, integrity, and availability risks created by autonomous decision-making. Real-world examples include NeuralTrust’s discovery of malicious URL instructions bypassing OpenAI Atlas validation and Zscaler ThreatLabz’s demonstration of hidden instructions on fake Python documentation sites that tricked models into making cryptocurrency payments. Data poisoning research from Carnegie Mellon and Cornell Tech showed agents from Anthropic, OpenAI, and Google selecting tampered datasets in half of tested cases. Additional findings cover vulnerabilities in Git MCP Server and comparative weaknesses between Function Calling and Model Context Protocol architectures. OWASP’s Agentic Top 10 and joint guidance from Canada, Australia, the US, New Zealand, and the UK emphasize threat modeling, least-privilege isolation, and separation of trusted versus untrusted data.

Three-Phase Defense Model OGL-Mini Protects AI Agents from Prompt Injection and Modern LLM Threats

The article presents OGL-Mini, an open-source hybrid security model designed to defend AI agents, chatbots, and RAG systems against contemporary threats including prompt injection, system prompt leakage, and agentic attacks. It details real-world incidents from 2025-2026 involving Microsoft Copilot Studio, OpenAI Atlas, and Claude Code, showing how attackers bypass safety filters using structured formats and obfuscation. OGL-Mini employs a three-stage pipeline of heuristics, TF-IDF mini-classifier, and PII detection to intercept malicious inputs before they reach the LLM. The model was trained on over 110,000 examples covering OWASP LLM01 categories, agentic misuse, and modern obfuscation techniques. Available in TypeScript, Python, and Go, it runs efficiently on standard CPUs with low latency. The solution aims to address gaps in built-in LLM safeguards that remain vulnerable to techniques like Policy Puppetry.