A Python library that lets API providers inject agent-targeted instructions into their JSON responses.
Clever hack for the agent economy, but feels like SEO spam for LLMs.
API providers looking to monetize AI agent traffic
Prompt injection tools · SEO meta tag optimizers
It replaces real API keys with short proxy tokens (e.g. PROXY:openai) and uses an X-Upstream-Host allowlist to ensure containers can only hit approved endpoints — neat and low-friction. Comes as a tiny Rust CLI with init/secret set/start commands and clear SDK examples, so you can bolt it onto OpenClaw or Docker agents without adopting a full secrets vault.
Auto-patching LLM calls to inject faults and log telemetry is a neat technical trick that lets you fuzz real agent runs without changing your stack. The repo ships six intentionally vulnerable example agents and a CLI (discover/run/ci) with eval packs for security and resilience, so you can reproduce attacks and gate releases. It feels like an early, practical toolkit that fills a gap in agent security testing — adoption and more community-playbooks will determine how far it goes.
Spring-style config injection for Python when pydantic-settings already handles this.
Another guardrail API competing with Lakera, but claims sub-30ms latency.
Three-line API integration blocks risky trades, cuts max drawdown 29% while improving win rate.
Agents never see real keys, but Vault already does secret injection.
