Install command
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Source-backed rules for reviewing AI-generated endpoints and data-access code before merge for insecure direct object reference risk, covering per-request object-level authorization checks, scoped database lookups, identifier exposure, and consistent enforcement across read, write, and admin operations.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
0
78
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
Registry metadata indicates a reviewed listing.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Install command
Not provided
Config snippet
Not provided
Copy snippet
Provided
Prerequisites
4 to clear
Platforms
1 listed
Install type
Copy & paste
Adoption plan
Current risk score 16/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
Listing has review metadata.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Required evidence gates are covered (5/6 signals complete).
Source repository/provenance is listed.
Required in this preset
Review metadata is present.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required evidence gates are covered for this preset.
Decision timeline
5/6 steps complete with no blocking gaps for this preset.
triage
Source/provenance metadata is available.
triage
Review metadata is available.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
No required blockers for this timeline preset.
Prerequisite readiness
4 prerequisites to line up before setup. Have accounts and credentials ready first.
Safety & privacy surface
3 safety and 3 privacy notes across 3 risk areas. Review closely: permissions & scopes, network access.
You are reviewing AI-generated code for insecure direct object reference
(IDOR) / broken object-level authorization risk.
Rules:
1. For every operation that looks up an object by an identifier from the
request (URL path, query string, or body), confirm there is a
server-side check that the current user is actually allowed to access
that specific object — not just that they are authenticated.
2. Prefer scoped lookups over raw lookups: fetch through the current user's
own relation (`current_user.projects.find(id)`) instead of a global
lookup by primary key (`Project.find(id)`) followed by an afterthought
permission check.
3. Apply the same authorization check to every operation on the object,
not just the one the happy path exercises — read, update, delete,
export, and any admin or bulk variant of the endpoint.
4. Do not trust a hidden form field, a client-supplied `user_id`/`ownerId`,
or any other identifier the client can edit as the source of truth for
whose data is being accessed; derive the acting user from server-side
session/auth state.
5. Do not treat an unguessable identifier (UUID, random token) as a
substitute for an authorization check — it raises the bar for guessing
but does not stop an attacker who already has or leaks a valid ID.
6. When a new route, resolver, or RPC method is added, confirm it goes
through the same authorization layer as sibling routes rather than
re-implementing an ad hoc check, and flag it if it skips a shared
authorization middleware/guard that similar existing routes use.Use these rules when an AI coding assistant writes or edits code that looks up, updates, deletes, or exports an object using an identifier supplied by the request. The goal is to stop a generated endpoint from shipping with a working happy path for the requester's own data while quietly allowing access to anyone else's data by changing an ID.
This is a review policy, not an IDOR tutorial. It tells reviewers what must be true about a generated change's authorization checks and lookup pattern before the change is safe to merge.
Collect enough context to know what object is being accessed and who should be allowed to access it.
user_id, an ownerId in the
request body, or a bearer claim the client can influence.Block merge when any of these is true.
user_id, editable claim) instead
of server-side session/auth state.AI assistants can write and review data-access code, but they should show their evidence.
Before adding a new authorization check, confirm the codebase does not already have a shared scoping helper, policy object, or middleware for this model. A generated route that writes its own inline permission check next to an existing shared guard should be treated as suspicious — check whether the existing mechanism was simply not reused before introducing a second, parallel, and possibly inconsistent one.
| Pattern | Risk | Fix |
|---|---|---|
Model.find(params[:id]) |
Returns any object regardless of owner | current_user.models.find(params[:id]) |
user_id read from request body |
Client chooses whose data is affected | Derive user from server-side session/auth |
Check only on the GET handler |
Update/delete/export siblings stay unauthorized | Enforce the same check on every verb for the object |
| Sequential numeric ID as the only barrier | Trivially enumerable | Non-guessable ID plus a real authorization check |
| Ad hoc inline permission check | Diverges from and can miss the shared policy | Route through the existing authorization middleware |
Show that AI-Generated IDOR (Broken Object-Level Authorization) Review Rules is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/rules/ai-generated-idor-review-rules)AI-Generated IDOR (Broken Object-Level Authorization) Review Rules side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
| Field | Source-backed rules for reviewing AI-generated endpoints and data-access code before merge for insecure direct object reference risk, covering per-request object-level authorization checks, scoped database lookups, identifier exposure, and consistent enforcement across read, write, and admin operations. Open dossier | Source-backed rules for reviewing AI-generated request handlers and forms before merge for cross-site request forgery risk, covering state-changing method discipline, anti-CSRF token correctness, SameSite cookie posture, origin and referer checks, and safe handling of cookie-based sessions. Open dossier | Source-backed rules for reviewing AI-generated code that deserializes data before merge for insecure deserialization risk, covering native serialization formats (pickle, PyYAML, Java Serializable) that can execute arbitrary code on untrusted input, safe data-interchange alternatives, and class allowlisting/integrity checks when native formats can't be avoided. Open dossier | Source-backed rules for reviewing AI-generated code that binds request parameters to model/entity objects before merge for mass assignment risk, covering allowlist field binding, DTOs that exclude sensitive fields, and the framework-specific autobinding features that make this easy to introduce by default. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | lourincedaging0-commits | jaso0n0818 | lourincedaging0-commits | lourincedaging0-commits |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | — | — | — |
| Category | rules | rules | rules | rules |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | lourincedaging0-commits | jaso0n0818 | lourincedaging0-commits | lourincedaging0-commits |
| Added | 2026-07-15 | 2026-06-22 | 2026-07-15 | 2026-07-15 |
| Platforms | Claude Code | Claude Code | Claude Code | Claude Code |
| Source repo | — | — | — | — |
| Safety notes | ✓A missing object-level authorization check lets any authenticated (or sometimes unauthenticated) user read, modify, or delete another user's data by changing an identifier in the request — accounts, documents, orders, invoices, and support tickets are common targets. AI assistants often generate a correct-looking handler for the current user's own data and skip the cross-user check entirely, because the happy-path test only ever exercises the requester's own objects. Switching to random/UUID identifiers reduces guessability but is not an authorization control; do not accept it as a substitute for a server-side ownership or permission check. | ✓A missing CSRF defense lets a malicious page perform state-changing actions as a logged-in user — transferring funds, changing email or password, or deleting data — using the victim's ambient cookies. AI assistants often generate handlers that work in tests yet omit token validation or perform state changes on GET, because the happy path succeeds without any forged cross-site request. Relying on SameSite cookies alone is not sufficient: defaults vary, Lax still allows top-level GET navigations, and some clients or legacy browsers do not enforce it. | ✓Deserializing untrusted data with a native format's full-featured API (pickle, unsafe YAML, Java Serializable, PHP unserialize) can cause denial-of-service or remote code execution — the vulnerability triggers during deserializing itself, before any application logic runs on the result. AI assistants often reach for the most convenient deserialization call (pickle for Python object graphs, yaml.load for config-like YAML, ObjectInputStream for Java) without checking whether the input is trusted, since developer-controlled test data works regardless of which API is used. An integrity check (signature/MAC) added after deserialization already ran does not help — the attack happens during deserialization, so the check must gate the deserialization call itself, not just the object it produces. | ✓Mass assignment lets an attacker set fields on a model that were never meant to be user-editable — a privilege flag, an ownership reference, a price — simply by adding an extra parameter the client controls to a request that already succeeds for legitimate fields. AI assistants often generate the shortest working binding code (bind the whole request body onto the model, or a framework's default autobinding) because it passes the happy-path test with only the intended fields present, without adding the allowlist/DTO layer that blocks unintended ones. This is not a theoretical risk: a mass assignment vulnerability in GitHub's own public-key update form in 2012 let an attacker add their SSH key to the rails organization and push code, entirely through an unintended request parameter. |
| Privacy notes | ✓IDOR proof-of-concept testing can expose another account's real data; use synthetic test accounts and synthetic objects rather than real user records when demonstrating the issue. Do not paste real user identifiers, documents, or other objects retrieved during testing into a public PR or issue; redact or replace them with placeholders. Server-side logs and error messages for a denied access attempt should avoid echoing back the unauthorized object's contents, only that access was denied. | ✓CSRF tokens are security credentials; do not paste real tokens, session cookies, or production request captures into public PR comments or issues. Use synthetic accounts and redacted requests when demonstrating a CSRF proof of concept, and avoid attaching real user identifiers. Be careful that anti-CSRF tokens are not written into URLs, analytics, or logs, where they can leak through referer headers or shared dashboards. | ✓Deserialization proof-of-concept payloads (e.g. a crafted pickle or Java serialized stream) can trigger real code execution in a test environment; run them only in an isolated, disposable sandbox, never against a shared or production system. Do not commit real crafted exploit payloads, credentials, or internal class/package names discovered while testing into a public PR or issue; describe the vulnerable pattern instead of attaching a working exploit. | ✓Mass assignment proof-of-concept testing (setting an unintended field like an admin flag) can grant real elevated access in a shared test environment; use an isolated account and environment, never a shared staging or production system. Do not commit a working exploit request (the exact extra parameter and target field) targeting a real internal endpoint into a public PR or issue; describe the vulnerable binding pattern and the class of field involved instead. |
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| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Source-backed guides for putting this to work.
Review AI-generated pull requests with repeatable security, test, and evidence checks.
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