Detecting and analyzing prompt abuse in AI tools
Microsoft Incident Response walks through how to detect prompt abuse operationally, tying prompt injection risk back to logging, telemetry, and incident response workflows.
Controls and attack paths for browsing, tool use, memory, identity, and action-taking agents.
Microsoft Incident Response walks through how to detect prompt abuse operationally, tying prompt injection risk back to logging, telemetry, and incident response workflows.
OpenAI frames prompt injection as an evolving agent-security problem that increasingly resembles social engineering rather than a simple string-matching issue.
MITRE maps incident patterns in an open-source agentic ecosystem to ATLAS techniques, showing how AI-first systems create distinct execution paths for attackers.
OpenAI describes using automated red teaming and reinforcement learning to discover agent prompt injection attacks before they appear in the wild.
Google Cloud outlines a defense-in-depth view of AI security spanning application controls, data protections, and infrastructure isolation.
Google’s CISO perspective on why agents need a new security paradigm and what changes when models can observe, plan, and act.
Google introduced AI Protection and Model Armor to address prompt injection, jailbreaks, data loss, and multicloud AI workload security.
The Operator system card documents red teaming and mitigation choices for a computer-using agent, with prompt injections listed as a central risk area.
Microsoft summarizes lessons from red teaming more than one hundred generative AI products, emphasizing system-level testing, human expertise, and automation.
OWASP’s GenAI security project remains a practical baseline for teams building or assessing LLM applications and agentic systems.