Prompt-Injection Testing Belongs in CI
ToolHazard turns indirect prompt-injection testing into executable, stateful evaluation. The useful lesson is not its leaderboard. It is how to make agent security a repeatable release gate.
notebook / 29 entries
I write about security problems, useful patterns, local tools, and what I learn when an agent has to do real work.
essential reading / foundation
Aur0ra operators reportedly persuaded a coding agent that real intrusions were authorized tests. The lesson is not simply that models can be fooled. Authorization has to exist outside the conversation.
ToolHazard turns indirect prompt-injection testing into executable, stateful evaluation. The useful lesson is not its leaderboard. It is how to make agent security a repeatable release gate.
Two Rovo disclosures show why agent governance cannot stop at an admin-console toggle. Security teams need to verify runtime capabilities, data reach, and egress independently.
GhostJacking shows how attacker-controlled text can cross from WAF events, logs, and error reports into privileged agent actions. The missing control is provenance from evidence to side effect.
ChainDrop used npm lifecycle scripts to steal credentials and spread, then planted Claude Code and VS Code configuration as secondary execution paths. Those files belong in the executable supply-chain surface.
Anthropic found three real intrusions inside cyber evaluations whose prompts claimed the internet was unavailable. A safe range needs machine-enforced scope, verified egress, and live boundary detection.
Hugging Face reconstructed roughly 17,600 actions across an autonomous intrusion. The durable lesson is a detection contract that joins agent, identity, Kubernetes, network, and source-control evidence.
Project Perception puts red, blue, and green agents into a closed security loop. Autonomous remediation needs separate identities, signed evidence, deterministic policy, rollback, and independent validation.
I added a Daemon to this site: a deliberately small, machine-readable projection of the private context I keep in LifeOS.
An OpenAI model evaluation became a real intrusion into Hugging Face. The useful lessons are about containment, credentials, ephemeral telemetry, and whether responders can analyze hostile evidence.
Five models produced the same 127 package-name candidates. Registry review narrowed them to 53 registrable slopsquatting targets, turning model hallucinations into a supply-chain watchlist.
CrowdStrike found viable detection signals for nine of fourteen SANDWORM_MODE behaviors, but only two were reliable enough to alert. The result shows where behavioral detection weakens and where provenance still helps.
A hidden pull-request comment turned legitimate MCP calls into a cross-project data path. Detecting it requires source, identity, scope, sequence, and sink.
A threat actor used an unattended AI agent inside a real intrusion. The useful lesson is in the evidence it added, not the capabilities it replaced.
I am using the Foundry Citadel reference platform to test how identity, network isolation, gateway policy, observability, and agent lifecycle controls fit together in a governed Azure deployment.
Four recent papers show why LLM security now has to cover memory, retrieval, tools, identity, delegation, interfaces, and the infrastructure around the model.
MITRE ATLAS gives AI security teams a shared map of adversary behavior for threat modeling, detection engineering, red teaming, and incident response.
The OWASP Agentic Top 10 moves security beyond model output and into goals, tools, identities, memory, delegation, and runtime control.
The OWASP LLM Top 10 turns common language-model failure modes into security requirements, tests, telemetry, and response plans.
NIST AI RMF gives an AI security program its operating model: govern the work, map the context, measure the risk, and manage what happens next.
Anthropic's framework connects agent identity, least agency, tool boundaries, memory integrity, and recovery into one practical security model.
Four failures in Aria's voice input taught me why a green build and a working development demo are not enough.
An audit of Aria's autonomous loop found hundreds of goals, almost no progress, and a completion system that rewarded plausible output.
Aria produced hundreds of useful research reports that nothing ever read. The fix was a small, idempotent consumer pipeline.
A self-observation feature for Aria showed why metrics should remain available without becoming permanent instructions.
What two rounds of testing Aria's language and image models taught me about speed, benchmarks, and knowing when a score is wrong.
A quick way to tell whether your agent setup is ready to grow or still held together by one-off fixes.
A practical security review for agents that can read files, run commands, and use outside services.
Four simple work patterns for agents that use tools, make changes, test the result, and recover from interruptions.
all tags