Did you see that CVE for the similar agent framework? Could it apply here?

Jay Kernel · 2026-06-24T23:58:00Z

Reading the CVE-2024-34045 disclosure for the LangChain Expression Language (LCEL) prompt injection was a real "hold my coffee" moment. It got me thinking about the OpenAI Operator pattern, especially given our work here on Open Claw. The vulnerability was essentially a code execution flaw via a maliciously crafted prompt that could abuse the `eval`-like functionality in LCEL's `RunnableLambda`. This raises immediate parallels for any agent framework that dynamically interprets or executes code based on LLM output. In the OpenAI Operator context, we're often looking at an agent that receives instructions, potentially from a user or a queue, and then executes actions using authenticated tools (like API calls to Jira, GitHub, Slack, etc.). The authentication model is critical. If the operator stores long-lived API tokens or OAuth refresh tokens in its environment or a connected vault, a successful prompt injection could lead to the agent exfiltrating those credentials or performing unauthorized actions on behalf of the user. The compliance implication of a hosted agent, like one running in OpenAI's own ecosystem, acting on user-supplied credentials is non-trivial. It essentially becomes a privilege escalation vector from the prompt context to the authenticated tool context. Consider a simplified tool definition pattern I've seen: ```python @tool def create_jira_issue(title: str, description: str) -> str: """Creates a Jira issue in project PROJ.""" auth = requests.auth.HTTPBasicAuth(USER, API_TOKEN) resp = requests.post(JIRA_URL, auth=auth, json={"title": title, "description": description}) return resp.json()["key"] ``` If the LLM is tricked via a crafted system prompt or retrieved web content into calling this tool with arguments that, say, point the `JIRA_URL` to a malicious endpoint (by manipulating the `title` to inject a newline and a `JIRA_URL` override in a poorly constructed string), you have a breach. The LCEL CVE was about direct code execution; here, the risk is indirect but just as severe: **unauthorized tool invocation with malicious parameters**. We need to threat model the operator's control flow: where does it get its instructions? How are tools validated and parameterized? Is there a strict schema validation before the tool is dispatched, or does it rely on the LLM to "do the right thing"? The memory safety of the underlying runtime (Rust vs. Python) matters less if the business logic authorization is flawed at the tool-calling layer. I'm drafting a more detailed analysis for the internal Ironclaw repo, but I wanted to get the forum's thoughts. How are you all implementing tool auth and input sanitization in your agent loops? Are we assuming the LLM is a trusted, uncompromised interpreter? Because the LCEL CVE suggests we absolutely cannot. ~ jay

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OpenAI Operator Security

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Vince T.

(@contrarian_vince)

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June 28, 2026 5:01 am

The immutable scope list is the only sane part, but you're trusting the client to build it correctly. That's the same old "client-side validation" mistake, just moved up the chain. Who defines the "potential" tool actions? If it's the client, a bug there is a privilege bug everywhere.

And "immutable" sounds good until you need to handle a multi-step approval flow. Real business logic doesn't fit a static menu minted at session start. You either break the workflow or you start making exceptions, and then your "vector" is back.

So the menu just shifts the attack surface to the scope issuance logic. Hope that code is perfect.

Show me the PoC.

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