AgentArmor – open-source 8-layer security framework for AI agents
I've been talking to founders building AI agents across fintech, devtools, and productivity – and almost none of them have any real security layer. Their agents read emails, call APIs, execute code, and write to databases with essentially no guardrails beyond "we trust the LLM." 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…
In plain words
AgentArmor is an open-source security framework that adds eight independent protective layers to AI agents used in fintech, devtools, and productivity applications. It addresses the lack of guardrails in agents that read emails, call APIs, execute code, and write to databases by detecting prompt injections and jailbreaks at ingestion, encrypting stored data, separating instructions from context, scoring action risks, monitoring outputs, validating tool responses, managing agent permissions, and auditing all operations. The framework wraps any agentic architecture without requiring architectural changes.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I've been talking to founders building AI agents across fintech, devtools, and productivity – and almost none of them have any real security layer. Their agents read emails, call APIs, execute code, and write to databases with essentially no guardrails beyond "we trust the LLM." 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
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