Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/thejefflarson/soundcheck/mass-assignmentnpx skills add thejefflarson/soundcheck --skill mass-assignmentgit clone --depth 1 https://github.com/thejefflarson/soundcheckWhat 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 | $0.00069 | $0.00748 |
| Opus 5 | $0.00034 | $0.00374 |
| Sonnet 5 | $0.00014 | $0.00150 |
| Haiku 4.5 | $0.00007 | $0.00075 |
Grade A, and why
mass-assignment 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 2d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mass Assignment Check (API3:2023)
What this checks
Protects against mass assignment (also called auto-binding or object injection) where
an attacker adds unexpected fields like role=admin or is_verified=true to a request
body and the ORM blindly persists them. Exploitation leads to privilege escalation,
account takeover, and data corruption.
Vulnerable patterns
- ORM create or update call that spreads, merges, or destructures the raw request body, deserialized payload, or query parameters into the model
- DTO-to-entity copy utility invoked with no field allowlist or exclude list, copying every matching field
- Decoded payload bound directly into a database struct or record that includes privileged columns
- Endpoint that lets the caller set fields like role, permissions, admin flags, verification status, balance, or tenant id from the payload
Fix immediately
Flag the vulnerable pattern and explain the risk. Then suggest a fix that establishes these properties:
- No ORM create/update call receives the raw request body. Requests land in a dedicated input type (DTO, validated schema, typed struct, sealed class) that contains only the fields external callers may set. Fields the input type does not mention are silently dropped by the deserializer.
- Privileged fields are set server-side, never from input. Roles, permissions, admin flags, verification status, balances, owner ids, and tenant ids come from the authenticated session or database defaults — never from the payload, even after "validation".
- DTO-to-entity copy utilities copy only named fields or explicitly exclude protected ones. A blanket field-by-field copy with no ignore list is the exact bug — the safe form names the fields.
- The allowlist lives next to the type, not scattered at call sites. A filter set repeated at every endpoint is brittle; the typed input pattern makes omission a compile-time (or deserialization-time) guarantee.
Translate these principles to the ORM, validation library, and deserializer of the audited file's language. Use the framework's documented allowlist or typed-input mechanism — do not hand-roll field filtering at the call site.
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.
- 2d ago First seen · 62 lines · 69 tokens per session scan A 5761d42cc264
mass-assignment is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 748 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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