Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
OpenAI documents its Moderation API as free to use. That removes a per-request moderation charge, while generated tokens and application infrastructure remain separate costs. Review queues and storage do too. Retries and policy operations are also separate costs. A sound budget tracks those components beside the endpoint result instead of treating free classification as a free moderation program.
Read post →AI threat modeling starts with assets and trust boundaries, then traces attacker paths across data, model, retrieval, agent, tool, identity, and application components. This guide combines NIST adversarial-machine-learning terminology with MITRE ATLAS techniques and turns the result into testable abuse cases with control owners and evidence requirements.
Function calling gives a model a schema for requesting an application action. MCP standardizes the session between a host and servers that expose tools and resources, plus prompts. This comparison traces discovery, invocation, transport, consent, and audit ownership so engineering teams can choose one or compose both without merging separate security boundaries.
EchoLeak, tracked as CVE-2025-32711, showed how an email could place hostile instructions inside Microsoft 365 Copilot retrieval context and turn a trusted answer into a data-exfiltration path. This analysis separates the injection, retrieval, model, rendering, and network boundaries so security teams can assign controls without overstating what any one gateway can see.
Read post →Attribute-based access control can turn an AI usage policy into a per-request decision. The useful design binds a verified subject, model route, requested operation, prompt classification, and current environment to a versioned rule. This guide defines the attribute contract, deny behavior, test cases, decision record, and the boundary between model authorization and downstream tool authorization.
CISA AI guidance organizes secure AI system development across design, development, deployment, and operation. Teams should turn those phases into one release record containing the threat model, asset and supplier evidence, deployment approval, monitoring plan, and named control owners while keeping model, infrastructure, and HTTP request controls assigned to the systems that can enforce them.
Read post →An AI agent kill switch needs a defined shutdown sequence across intake, model routes, delegated credentials, tools, workers, and queued work. This guide turns that sequence into an engineering control with named owners, activation thresholds, preserved evidence, and a restart gate.
A Crescendo attack builds a jailbreak across several benign-looking turns, using the model''s own replies as material for the next escalation. Defending against it requires conversation-aware testing and policy, plus a clear account of what an HTTP gateway can inspect when providers keep server-side state.
Read post →ASCII smuggling uses non-rendering Unicode tag characters that mirror printable ASCII, allowing text to carry a hidden instruction while looking ordinary on screen. Security controls should inspect the raw code points, normalize or reject unexpected tags, constrain model authority, and preserve the original and transformed request evidence.
Read post →Many-shot jailbreaking fills a long context with fabricated dialogues in which an assistant repeatedly complies with requests it would normally refuse. The demonstrations steer the model toward compliance on a final harmful request. Production defenses need context-aware testing, request limits, independent policy, and evidence beyond provider refusal behavior.
Read post →An AI kill chain maps an adversary operation as transitions across reconnaissance, access, model interaction, execution, collection, and impact. The useful control work happens at those transitions. This guide maps evidence and ownership while keeping HTTP request-boundary enforcement separate from endpoint, identity, and model-development controls.
Read post →The AI Incident Database collects public reports of harmful or potentially harmful AI behavior so researchers and practitioners can find recurring failure patterns. Security teams can use it for scenario discovery and control design, while keeping internal incident records, evidence, and reporting decisions in their own governed systems.
Read post →