Writing on web security

Audit logging for ai agents is your detection layer: what to record per turn, how to spot bad tool use, and how…

Use mcp tool pinning to fingerprint each tool definition at approval and block silent changes, stopping rug pulls, poisoning, and tool shadowing.

Egress filtering for ai agents is the containment layer that stops stolen data leaving: domain allowlists, stripped markdown images, and inspecting proxies.

Use prompt injection canary tokens to catch LLM data leaks: plant secret markers, watch every exit, and get hard proof the instant…

How llm guardrail models screen the input and output of a main LLM, where these filters catch attacks, and the honest limits…

AI agent sandboxing runs agent generated code and tool calls in a confined environment so a compromised agent cannot reach the host,…

Human in the loop ai agents require explicit approval before sensitive actions, so a person, not an injected document, authorizes money, email,…

Apply least privilege for ai agent tools to shrink blast radius: scoped tokens, read write splits, tool allowlists, and short lived credentials.

The camel prompt injection defense from Google DeepMind stops attacks by design, using a plan as code, a quarantined model, and capability…

The dual llm pattern keeps untrusted text away from the model that calls tools, so injected instructions cannot drive real actions. Here…