ADR is an enterprise security system for AI agents that discovers AI tools, records agent activity, tests defenses, detects threats, and helps prevent unsafe actions. Organizations use it to secure employee-facing coding agents and customer-facing support agents; the catalogue contains one skill related to ADR.
Borrowing it
Nothing to install: this file belongs to uber/ADR. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/uber/ADR/main/.claude/skills/submit-malicious-skill/SKILL.mdgit clone --depth 1 https://github.com/uber/ADRWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/uber/adr/submit-malicious-skill)<a href="https://agentmods.dev/skills/uber/adr/submit-malicious-skill"><img src="https://agentmods.dev/badge/skills/uber/adr/submit-malicious-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/uber/adr/submit-malicious-skill"><img src="https://agentmods.dev/badge/skills/uber/adr/submit-malicious-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.00768 |
| Opus 5.5 | $0.00026 | $0.00307 |
| Sonnet 5.5 | $0.00013 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
submit-malicious-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report a malicious skill
You prepare one GitHub issue on the ADR repository. The skill's zip is attached to the issue on GitHub after it is created.
1. Identify the skill
You need its folder hash (skill_sha256, 64 hex characters) and its zip.
- Take both from a Discovery snapshot: skill assets carry
skill_sha256,install_path,provenanceand, when the scan ran with--archive-skills,archive.path. - No snapshot, or no archive? Scan with archiving on:
adr-discovery --output-dir OUT --archive-skills(orpython -m adr_discovery.cli ...fromDiscovery/), and read the skill from the newestOUT/snapshot-*.json. - Check the zip is that skill. This must print the
skill_sha256:python -c "import sys; from adr_discovery.archive import zip_content_sha256; print(zip_content_sha256(open(sys.argv[1], 'rb').read()))" <zip>
Ask the user in their own words why the skill is malicious, and for any public links that back it up.
If provenance shows a private or local marketplace, ask whether the skill
belongs to their organisation. If it does, it should be reported there, not
here.
2. Prepare the issue
Title: Malicious skill: <name> (<first 12 hex of skill_sha256>)
Body:
**Skill:** `<name>`
**Folder hash (`skill_sha256`):** `<skill_sha256>`
**Source:** <marketplace and plugin with version, and the marketplace repo, if public; otherwise "local or private">
**Seen at:** `~/<home-relative install path>`
**Why it is malicious**
<the user's reason>
**References**
- <https links, if any>
**Verify the attached zip**
`zip_content_sha256` of the zip must print the folder hash above:
`python -c "import sys; from adr_discovery.archive import zip_content_sha256; print(zip_content_sha256(open(sys.argv[1], 'rb').read()))" <skill_sha256>.zip`
**Proposed indicator**
```json
{"id": "skill-<name>-<first 12 hex>", "status": "active", "kind": "skill_sha256",
"summary": "<one sentence: what the skill does>", "references": [...],
"target": {"sha256": "<skill_sha256>"}}
```
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 74 lines · 65 tokens per session scan A db97cbdf09bf
submit-malicious-skill is a skill published in the GitHub repository uber/ADR (2,083 stars, last pushed 3d ago), licensed Apache-2.0. It adds 65 tokens to every session and 768 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-14.
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