So I built AgentArmor: an open-source framework that wraps any agentic architecture with 8 independent security layers, each targeting a distinct attack surface in the agent's data flow.

The 8 layers: L1 – Ingestion: prompt injection + jailbreak detection (20+ patterns, DAN, extraction attempts, Unicode steganography) L2 – Storage: AES-256-GCM encryption at rest + BLAKE3 integrity for vector DBs L3 – Context: instruction-data separation (like parameterized SQL, but for LLM context), canary tokens, prompt hardening L4 – Planning: action risk scoring (READ=1 → DELETE=7 → EXECUTE=8 → ADMIN=10), chain depth limits, bulk operation detection L5 – Execution: network egress control, per-action rate limiting, human approval gates with conditional rules L6 – Output: PII redaction via Microsoft Presidio + regex fallback L7 – Inter-agent: HMAC-SHA256 mutual auth, trust scoring, delegation depth limits, timestamp-bound replay prevention L8 – Identity: agent-native identity, JIT permissions, short-lived credentials

I tested it against all 10 OWASP ASI (Agentic Security Integrity) risks from the December 2025 spec. The red team suite is included in the repo.

Works as: (a) a Python library you wrap around tool calls, (b) a FastAPI proxy server for framework-agnostic deployment, or (c) a CLI for scanning prompts in CI.

Integrations included for: LangChain, OpenAI Agents SDK, MCP servers.

I ran it live with a local Ollama agent (qwen2:7b) – you can watch it block a `database.delete` at L8 (permission check), redact PII from file content at L6, and kill a prompt injection at L1 before it ever reaches the model.

GitHub: https://github.com/Agastya910/agentarmor PyPI: pip install agentarmor-core

Would love feedback, especially from people who have actually built production agents and hit security issues I haven't thought of.

TAGS: security, python, llm, ai, agents