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Community launch — AgentGuard 1.2.0

Ready-to-paste posts for Show HN, dev.to cross-post, and follow-up comments. Publish dev.to first if you want a canonical article URL to link from HN.

Last updated: 2026-08-23.

Show HN

URL to submit: https://github.com/nizba06/agentguard

Title (pick one):

Show HN: AgentGuard – open-source firewall for inter-agent AI messages

Show HN: AgentGuard – Ed25519 signing and capability manifests for multi-agent pipelines

Post body (optional — GitHub URL alone is fine on Show HN):

Multi-agent systems pass messages between orchestrator, researcher, writer, etc.
Nobody inspects those hops. A poisoned web fetch or forged handoff can hijack
the pipeline without touching the user prompt.

AgentGuard is a pip-installable Python middleware:
- Content inspection (rules + DeBERTa ONNX + consistency)
- Ed25519 signatures on inter-agent messages
- YAML capability manifests (forbid tools like publish_external)
- MCP tool output inspection

pip install inter-agent-guard

Two example pipelines in-repo: vulnerable (attack succeeds) vs secured (blocked).
IntelBrief demo repo runs a three-agent LangGraph pipeline locally.

Honest limits published — we withdrew a 99.4% metric after finding the v1 scorer
classified on message length. v1.2.0 retrains + prefix scoring; quote deepset
test 90% detection / 0% FPR, not holdout headlines.

Looking for feedback from people building LangGraph / LangChain agent teams.

First comment (post immediately after submitting)

Author here. Quick links:

GitHub: https://github.com/nizba06/agentguard
PyPI: https://pypi.org/project/inter-agent-guard/
Release (ONNX): https://github.com/nizba06/agentguard/releases/tag/v1.2.0
Local demo: https://github.com/nizba06/inter-agent-guard-demo

Try in 2 commands:
  pip install inter-agent-guard
  python examples/vulnerable_pipeline/pipeline.py   # attack succeeds
  python examples/secured_pipeline/pipeline.py      # blocked

Strongest deterministic layers (no ML required): Ed25519 trust + capability
manifests. ML is transfer-tested on deepset prompt-injections test (90%/0% FPR)
with published bypasses: https://github.com/nizba06/agentguard/blob/master/docs/KNOWN_LIMITATIONS.md

OWASP-oriented threat model + reviewer packet:
https://github.com/nizba06/agentguard/blob/master/docs/OWASP_REVIEWER_PACKET.md

Happy to answer integration questions (LangChain middleware, LangGraph wrap,
MCP tool wrapping). CPU ML P95 ~3s — rules-only or monitor mode if latency matters.

dev.to (if not published yet)

Use DEVTO_BLOG_POST.md. Set title to:

AgentGuard 1.2: An Open-Source Firewall for Inter-Agent AI Messages

Tags: ai, security, python, opensource

After publishing, add the dev.to URL to your Show HN first comment.

Reddit (optional)

r/programming or r/MachineLearning — use a shorter version:

Title: I built an open-source firewall for messages between AI agents (LangGraph/LangChain)

Body: Problem paragraph + three layers bullet list + GitHub link + honest limits link. Do not lead with 99.4% or holdout 92%.

Launch checklist

  • dev.to published (optional but recommended before HN)
  • Show HN submitted (Tue–Thu morning US time tends to work best)
  • First comment posted with links
  • Render intelbrief-demo deleted (RENDER_SUNSET.md)
  • Monitor GitHub issues and HN thread for 24–48 h
  • Reply to technical questions with links to THREAT_MODEL / KNOWN_LIMITATIONS

What not to say

Avoid Say instead
99.4% detection Withdrawn — v1 length artifact
Holdout 92% as general PI detection In-distribution regression gate only
Full OWASP ASI compliance Partial coverage — see OWASP_ASI_MAPPING.md
Hosted demo URL Local clone — PUBLIC_DEMO.md
"Stops all prompt injection" deepset test 90%/0% FPR with documented bypasses

Suggested reply snippets

"How is this different from Microsoft Agent Governance Toolkit?"
→ https://github.com/nizba06/agentguard/blob/master/docs/MICROSOFT_TOOLKIT_COMPARISON.md
Content-aware ML + MCP output inspection vs enterprise governance CLI.

"Why not just prompt the model to be safe?"
Indirect injection enters via tool output / fetched pages — never in the user prompt. Trust + capability layers are deterministic even when ML misses.

"Latency?"
~3.3 s P95 ML on CPU INT8. Rules-only (require_ml_model=False) or monitor mode for shadow deploy. See docs/source/latency.md.