Prompt Injection Against Coding Agents Is Already in the Wild: Scanning 86 Million GitHub Items and the Fake AGENTS.md in Downloads

By Carlos Montiel | Enterprise AI Specialist
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Published: 2026-10-28 | By: Carlos Montiel | Reading time: ~5 minutes
Prompt injection against coding agents is in the wild: 86 million GitHub items scanned and a fake AGENTS.md in Downloads

Two reports this week show that attacking a coding agent no longer requires breaking into it: you only have to leave it text that it decides to obey on its own.

Radware's Scan: 86 Million GitHub Items

According to CYBR.SEC.Media, Radware researchers Gabi Nakibly and Zvika Babo presented a scan of 86 million GitHub items at the [un]prompted II conference (San Francisco): issues, pull requests, comments and README files. They extracted hidden text, such as HTML comments, and classified it with heuristics, a two-stage LLM classifier and manual review.

Items scanned 86 million Injection frequency ~2.5 per 100,000 items Expanded repository set ~145 million items, 47 attack attempts identified (figures separate from the main talk dataset)

The hidden instructions fell into four categories: agent orchestration (legitimate project automation), restrictions for visiting bots, "growth hacking" (promoting projects or shaping how agents describe them) and malicious activity, including attempts to exfiltrate data. The authors themselves caution that the scan covered only hidden text and doesn't establish how many attacks succeeded.

The Live Case: AGENTS.md in the Downloads Folder

According to OODAloop, the firm Forever Security intercepted real malware that drops Markdown instruction files, such as AGENTS.md, into the user's Downloads directory, disguised as engineering notes. When a developer launches a coding agent in that directory, the agent reads the file as project context and can rewrite configurations without authorization. OpenAI's Codex and Claude Code are mentioned. Note: we couldn't open the original report cited (Dark Reading), so the detail comes from that summary.

Why a Patch Doesn't Fix It

The researchers point out that the agents work "as designed": reading local instruction files is a feature, not a bug. That's why the labs can't close the vector with a patch. Radware's conclusion sums it up: "Attackers don't need to compromise an AI coding agent to manipulate it." It's a trust-architecture problem, like SQL injection, but without an equivalent parameterized query yet.

Controls recommended in the sources: treat repository content as untrusted input; verify authority outside that content; limit the agent's privileges; monitor the agent's actions, not just its inputs; treat download folders as hostile environments; use a strict sandbox and periodically audit configurations and credentials.

What It Means If Your Company Uses Coding Agents

What it means for your company: if your developers use Codex, Claude Code or similar tools, define where they may run (never in Downloads or in cloned repos that haven't been reviewed), what permissions they have without human approval, and which credentials they can see. Test your workflows against a repository containing malicious hidden instructions before an attacker does. For a review of agent policies, write to info@guatemalia.com.

Sources: CYBR.SEC.Media (October 27, 2026), OODAloop (October 27, 2026, citing Forever Security).

Carlos Montiel
Enterprise AI Solutions Architect
Specialist in LLMs, Agents, and Orchestration
guatemalia.com/#contacto · info@guatemalia.com

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Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.

Contact Carlos Montiel

info@guatemalia.com