auditing-ml-inference-endpoint-abuse SKILL.md skillA
auditing-ml-inference-endpoint-abuse from unboundcompute/security-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add unboundcompute/security-agent-skills/auditing-ml-inference-endpoint-abuse@git:20260901.f8395d1Pins git:20260901.f8395d1 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- unboundcompute/security-agent-skills
- Path
skills/auditing-ml-inference-endpoint-abuse/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260901.f8395d1, committed Sep 1, 2026
- Size
- 9,780 bytes
- Repository stars
- 26
- License
- MIT
Audit a hosted model inference endpoint for abuse that costs money or steals the asset: an unauthenticated or weakly keyed endpoint anyone can call, no per-caller rate or spend limit so a caller runs up unbounded inference cost, model extraction where systematic queries reconstruct the model or its decision boundary, membership and training-data inference that recovers whether a record was in the training set, and a response that returns full probabilities or embeddings that make extraction…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Auditing ML inference endpoint abuse: a served model is an asset and a meter, both attackable A model exposed as an inference endpoint is two things worth attacking at once: a metered resource that costs money per call, and a confidential asset that queries can reconstruct. The abuses follow from that. If the endpoint is unauthenticated or weakly keyed, anyone can call it. If there is no per-caller rate or spend limit, a caller runs up unbounded inference cost, a denial-of-wallet against expensive model serving. Beyond cost, the model itself leaks to a determined querent: systematic queries reconstruct the model or its decision boundary (model extraction), and carefully chosen queries recover whether a specific record was in the training set or reconstruct sensitive training data (membership and inversion inference). Over-informative responses, full class probabilities, raw embeddings, confidence vectors, make both extraction and inversion far easier. The audit treats the endpoint as a cost meter and a confidential asset and checks the controls on both. You audit this by calling the endpoint as an attacker would: unauthenticated, at volume, and systematically. ## When to use …
Read the whole file at its exact version.
How to install
mdr add unboundcompute/security-agent-skills/auditing-ml-inference-endpoint-abuse@sha256:1ceed4a31f975470The line at the top of this page pins the label git:20260901.f8395d1, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_ihxdvdpixex5q5ql)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Sep 1, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (9780 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
unboundcompute/security-agent-skills · 26 stars · license MIT · pushed 2026-09-08 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ihxdvdpixex5q5ql GET https://markdownregistry.com/api/v1/resolve?ref=unboundcompute/security-agent-skills/auditing-ml-inference-endpoint-abuse GET https://markdownregistry.com/api/v1/blob/1ceed4a31f9754705fbb07f57e9aa3f7988a37997b72c9c1633afc0862751044
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.