Show HN: A Firewall for AI agents with auditing

The emergence of AI agents operating autonomously on the internet has created a new security challenge: how to monitor and control their behavior without introducing unacceptable latency or reliability issues. A developer on Hacker News has proposed a solution that bypasses the conventional approach of using a large language model (LLM) as a guardrail, instead building a dedicated firewall in Rust that claims to execute in under five milliseconds.

What Was Announced

The project introduces a specialized firewall designed specifically for AI agents, incorporating auditing capabilities. The key innovation is its rejection of LLM-based guardrails, which the developer identifies as problematic due to two well-documented issues: hallucination (where the guardrail LLM incorrectly flags or allows actions) and latency (the time required for an LLM to process and respond to agent actions). By writing the firewall in Rust, the developer prioritizes deterministic performance and low overhead, targeting sub-five-millisecond execution times per check. The summary does not detail the exact filtering mechanisms, but the implication is a rule-based or pattern-matching system rather than a probabilistic one.

Why This Matters

This development addresses a fundamental tension in the current AI agent ecosystem. Most security solutions for agents rely on another agent or an LLM to act as a gatekeeper, creating a recursive dependency: you need an LLM to guard an LLM. This introduces both the hallucination risk—where the guardrail might misinterpret an agent’s intent—and the latency penalty, which can compound as agents make many rapid decisions. For production deployments, especially in financial trading, automated customer support, or code execution environments, even a few hundred milliseconds of overhead per action can degrade user experience or miss time-sensitive opportunities.

The Rust-based approach suggests a shift toward more traditional, deterministic security tooling for AI systems. If the firewall can reliably block malicious or unintended agent actions without the computational cost of an LLM call, it could become a standard component in agent infrastructure. This mirrors how web application firewalls (WAFs) evolved from simple rule sets to more complex systems, but here the target is agent behavior rather than HTTP requests.

Implications for AI Practitioners

For developers building agentic systems, this project highlights a critical design choice: where to place security logic. Relying on an LLM for guardrails is convenient but introduces a failure mode that is hard to audit—if a guardrail LLM hallucinates, it may silently allow or block actions. A deterministic firewall, by contrast, provides clear, auditable logs. Practitioners should consider a layered approach: a fast, deterministic firewall for high-frequency, low-risk checks, with an LLM-based guardrail reserved for complex, ambiguous decisions where context matters.

The auditing feature is equally important. As regulations around AI accountability tighten, having a tamper-proof log of agent actions and firewall decisions will become a compliance necessity. This project implicitly argues that security for AI agents should be built on the same principles as traditional cybersecurity—speed, determinism, and auditability—rather than relying entirely on the same technology being secured.

Key Takeaways

• A Rust-based firewall for AI agents offers sub-five-millisecond execution, avoiding the latency and hallucination risks of LLM-based guardrails.
• The project signals a maturation of the AI agent ecosystem, where security moves from experimental LLM-based solutions toward deterministic, auditable infrastructure.
• AI practitioners should evaluate a hybrid security model: fast rule-based firewalls for routine checks, with LLM guardrails reserved for context-dependent decisions.
• Auditing capabilities in agent firewalls will become critical for compliance as regulatory scrutiny of autonomous AI systems increases.