GUARDPOST
Runtime agent firewall — PII redaction, rate limits, policy enforcement
AI Security & Governance — securing LLMs, agents, and the MCP supply chain.
pip install cognis-guardpost
guardpost scan . # → prioritized findings in seconds
🔎 Example output
Real, reproducible output from the tool — runs offline:
$ guardpost-emit --version
guardpost 0.1.0
$ guardpost-emit --help
usage: guardpost [-h] [--version] [--format {table,json}] <command> ...
Runtime agent firewall: PII redaction, policy enforcement, rate limits.
positional arguments:
<command>
scan scan text/file for PII & policy violations
options:
-h, --help show this help message and exit
--version show program's version number and exit
--format {table,json}
output format (default: table)
Blocks above are real
guardpostoutput — reproduce them from a clone.
Sample result format (illustrative values — run on your own data for real findings):
{
"guardpost": {
"platform": "stix",
"findings": [
{
"id": "1234567890",
"name": "Suspicious Network Traffic",
"description": "Potential malicious activity detected on network 192.168.1.100",
"created_by": "John Doe",
"created_at": "2023-02-20T14:30:00Z"
},
{
"id": "2345678901",
"name": "Malware Detection",
"description": "Virus detected on system with IP address 10.0.0.1",
"created_by": "Jane Smith",
"created_at": "2023-02-21T16:45:00Z"
}
]
}
}
Usage — step by step
- Install (Python 3.9+):
pip install guardpost - Scan a file (or stdin) for PII and policy violations; redaction is on bydefault:
guardpost scan request.txt cat prompt.txt | guardpost scan - - Tighten the policy: use the strict preset, ban terms, set a rate limit, ortag the principal for audit:
guardpost scan request.txt --strict --rate-limit 60 --principal svc-bot \ --ban "internal-secret" --ban "api_key" - Read the output: the table shows ALLOWED/BLOCKED, the findings(severity/category/kind/excerpt) and the sanitized text. The process exits
2when traffic is BLOCKED,0when ALLOWED — wire that into a pipeline. - Emit machine-readable findings for a gateway / CI:
cat prompt.txt | guardpost scan - --strict --format json
Contents
- Why guardpost? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing
Why guardpost?
Runtime agent firewall — PII redaction, rate limits, policy enforcement — without standing up heavyweight infrastructure.
guardpost is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.
Features
- ✅ Scan Pii
- ✅ Redact
- ✅ Scan Policy
- ✅ Fingerprint
- ✅ Guard
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (
ports/)
Quick start
pip install cognis-guardpost
guardpost --version
guardpost scan . # scan current project
guardpost scan . --format json # machine-readable
guardpost scan . --fail-on high # CI gate (non-zero exit)
Example
$ guardpost scan .
[HIGH ] GUA-001 example finding (./src/app.py)
[MEDIUM ] GUA-002 another signal (./config.yaml)
2 findings · risk score 5 · 38ms
Architecture
flowchart LR
IN[agent / A2A traffic] --> P[guardpost<br/>map + analyze]
P --> OUT[graph + flags]
Use it from any AI stack
guardpost is interoperable with every popular way of using AI:
- MCP server —
guardpost mcp(Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet) - OpenAI-compatible / JSON — pipe
guardpost scan . --format jsoninto any agent or LLM - LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
- CI / scripts — exit codes + SARIF for non-AI pipelines
How it compares
| Cognis guardpost | protectai | |
|---|---|---|
| Self-hostable, no account | ✅ | varies |
| Single command, zero config | ✅ | ⚠️ |
| JSON + SARIF for CI | ✅ | varies |
| MCP-native (AI agents) | ✅ | ❌ |
| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |
| Open license | ✅ COCL | varies |
Built in the spirit of protectai/llm-guard, re-framed the Cognis way. Missing a credit? Open a PR.
Integrations
Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (guardpost mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.
Install — every way, every platform
pip install "git+https://github.com/cognis-digital/guardpost.git" # pip (works today)
pipx install "git+https://github.com/cognis-digital/guardpost.git" # isolated CLI
uv tool install "git+https://github.com/cognis-digital/guardpost.git" # uv
pip install cognis-guardpost # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/guardpost:latest --help # Docker
brew install cognis-digital/tap/guardpost # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/guardpost/main/install.sh | sh
| Linux | macOS | Windows | Docker | Cloud |
|---|---|---|---|---|
scripts/setup-linux.sh |
scripts/setup-macos.sh |
scripts/setup-windows.ps1 |
docker run ghcr.io/cognis-digital/guardpost |
DEPLOY.md (AWS/Azure/GCP/k8s) |
Related Cognis tools
aegis— AI Agent Permission & Access Auditor — surfaces the lethal trifecta of credentials + injection + reachpromptmirror— Prompt-injection & indirect-injection scanner for any LLM context inputledgermind— Local LLM cost & token forensics proxy with anomaly detectionadversa— LLM red-team harness — OWASP LLM Top 10 + MITRE ATLAS attack packshallumark— LLM hallucination & grounding auditor for RAG systemsaicard— Auto-generated NIST AI RMF / EU AI Act Annex IV model & system cards
Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram
Contributing
PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.
⭐ If
guardpostsaved you time, star it — it genuinely helps others find it.
Interoperability
{} composes with the 300+ tool Cognis suite — JSON in/out and a sharedOpenAI-compatible /v1 backbone. See INTEROP.md for thesuite map, composition patterns, and reference stacks.
License
Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.