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A causal firewall for AI agents: blocks multi-step tool-call chains that leak data, even when every call is individually allowed.

4 starsRust

Trajeckt, a Firewall for AI Agents

by beebeeVB·Jun 30, 2026·1 point·0 comments

AI Analysis

●●●BangerBig BrainWizardry

Causal enforcement catches multi-step exploits that per-action security misses.

Strengths
  • DAG-based trajectory tracking detects exfiltration across multiple legal-looking tool calls
  • ~1.6ms deterministic enforcement outside the agent's reach, no LLM hallucination risk
  • HMAC-sealed commitment graphs with Docker smoke tests prove the concept works end-to-end
Weaknesses
  • Early stage with zero forks and limited documentation beyond the demo
  • MCP-only support limits adoption to agents using that specific protocol
Category
Target Audience

AI agent developers, security engineers building agent systems

Similar To

Lakera Guard · Protect AI · Portkey

Post Description

Hi all, As there are more and more agents in the internet; Security is going to be a big problem. Currently, the problem is solved using a LLM to guard Agent but this creates the problem of hallucination and latency, so I coded a firewall in rust that runs under five milliseconds. This works by creating a plan and enforcing the plan; While the agent is running, we track the trajectory using graph of the tools and the actions it used and data it accessed. for per action call, this enforces using the Model context protocols list and for sequence it tracks every single tool call and data flow; there is also a taint mechanism where if the agent reads something outside of the user context, it flags and adds more security mechanism. It works by using a DAG.

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