On the same day that Oasis Security disclosed a critical vulnerability chain in OpenClaw, and an MIT study found that most agentic AI systems have no documented shutdown controls, a credible new open-source project arrived that addresses both problems at the design level.

IronCurtain — published today by Niels Provos, a security researcher with serious credentials (he’s known for work on OpenSSH and honeypot research) — is a model-independent security wrapper for LLM agents that enforces behavioral constraints without requiring changes to the underlying model.

The timing is not a coincidence. Provos has been working on the project for months, and today’s WIRED coverage bringing it to mainstream attention feels like a deliberate response to an increasingly urgent gap in the agentic AI stack.

What IronCurtain Does

The core idea is that you shouldn’t have to trust the LLM to behave correctly. Instead, you enforce constraints at the infrastructure level, so that even a badly prompted or actively manipulated agent cannot violate your policies.

IronCurtain wraps your agent with four core mechanisms:

1. Code Sandboxing

Any code generated by the LLM is executed inside an isolated sandbox — not directly on the host. The sandbox limits filesystem access, network calls, subprocess spawning, and system calls to an explicit allowlist. Code that tries to exfiltrate data, modify system files, or phone home simply can’t — the sandbox stops it before it happens.

2. Plain-English Policy Enforcement

Instead of writing security rules in code, you write them in plain English. IronCurtain parses these rules and enforces them on every agent action. Example policies from the documentation:

  • “Never send any email without showing me the full text first”
  • “Do not access any file outside of the project directory”
  • “Never make API calls to services not on this list: [list]”

The policy engine uses a small, fast classifier to evaluate each proposed agent action against the active policy set before allowing it to execute.

3. Credential Isolation

Credentials never enter the agent’s context. IronCurtain maintains a separate credential store and injects them at the infrastructure level — the agent can trigger an authenticated action, but it cannot read the credentials needed for that action. This directly addresses the credential theft attack surface that Oasis Security documented in the OpenClaw vulnerability chain today.

4. Audit Log with Constitution Refinement

Every action the agent attempts — whether allowed or blocked — is logged. The log captures the agent’s stated intent, the proposed action, the policy decision, and the outcome. Over time, IronCurtain can analyze edge cases from the log and suggest refinements to your policy constitution. You review and approve the suggestions; the system learns from real-world behavior.

Model-Independent Design

IronCurtain explicitly supports any LLM that exposes a standard tool-calling API. The wrapper sits between your orchestration layer and the model’s tool execution — it doesn’t care whether the model is Claude, GPT-4o, Gemini, Llama, or a local Ollama instance. If the model can emit tool call JSON, IronCurtain can intercept and gate it.

This is a significant design choice. Security tooling that’s model-specific creates lock-in and maintenance burden. IronCurtain’s model-agnostic approach means you can use it across a mixed fleet of agents.

Why This Matters Right Now

The MIT study published today found that the majority of agentic AI systems have no documented safety testing. Most have no defined shutdown mechanism for rogue agents. Most disclose nothing about how they handle failures.

IronCurtain is a concrete, deployable response to that gap. It doesn’t solve the alignment problem. It doesn’t make your agent smarter or more reliable. But it does give you a real engineering handle on what your agent is allowed to do — and that’s the thing most production deployments are missing.

Given the Oasis Security disclosure today, IronCurtain’s credential isolation feature is especially timely. Integrating it with an OpenClaw deployment would directly address one of the key attack vectors in the published vulnerability chain.

The project is available on GitHub. Provos has been responsive to early feedback since the project predated today’s WIRED coverage by about a day.

Sources

  1. WIRED — IronCurtain: Open-Source AI Agent Security (2026-02-26)
  2. provos.org — Author’s original announcement and documentation (2026-02-25)
  3. DNYUZ — IronCurtain coverage and community response (2026-02-26)

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260226-2000

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