Last week, I ran AINAScan — our AI-powered static analysis tool — against FlaskBlog, a popular open-source Flask project. It found two security issues back-to-back. Here's the breakdown.
Issue #1: IDOR — Direct Object Reference Without Authorization Check
The bigger finding was a classic IDOR (Insecure Direct Object Reference) vulnerability. A user could directly reference another user's resource by ID without any authorization check. This is issue #254.
Issue #2: Password Hash Leaked Into Template Context
The second finding — issue #258 — is subtler. In , the search results query fetches the full user row including the field, and that tuple gets passed directly into the template context:
The template currently only renders and . So no hash is displayed right now. But the data is there — one accidental in the template (during a future edit) would expose bcrypt hashes to every visitor.
Why This Pattern Is Dangerous
Most developers don't think twice about or selecting all columns for convenience. But every field you expose to the template layer is a surface area that can leak — through:
- A typo in a template ( vs )
- A future developer who doesn't know the context
- Debugging code that inadvertently dumps the full object
Principle of Least Privilege applies to data too. Only pass what the template actually needs.
The Fix (One Line)
How AINAScan Caught This
AINAScan traces data flow from the query result tuple through to the template render call. It flags cases where sensitive field names (like , , ) appear in a query but the result is passed to a render function without explicit field filtering.
No execution needed — pure static AST + data flow analysis.
The maintainer acknowledged the finding and is bundling the fix with the IDOR patch in v3. Closing as duplicate of #254.
Lesson
Before every call, ask: do I actually need all these fields? If your ORM returns a model object or a raw tuple with 10+ columns, consider projecting down to only what the view needs.
AINAScan is open-source and free to try. Drop your repo URL and see what it finds: github.com/moonsehwan/aina-scan
Do you explicitly filter query results before passing to templates, or do you SELECT * and let the template decide what to show?
Top comments (3)
The useful part of AI-assisted security scanning is not just finding a bug; it is producing a reviewable path from source to exploitability. For findings like IDOR, I would want the tool to show the route, the missing authorization boundary, and the minimal reproduction. Otherwise teams either over-trust or ignore the report.
"This is exactly what the AINA Advisor layer is built for — it traces the causal chain from source to sink and explains why the path is dangerous, not just that it is. For the IDOR case, it would show: user_id from request → direct DB query → no ownership check. We're working on surfacing this in the report output more explicitly. Thanks for the specific feedback."
That source-to-sink explanation is the part that would make me trust the report. For IDOR especially, the useful output is not just severity; it is the route, the missing ownership check, the exact parameter boundary, and the smallest reproduction that a maintainer can verify quickly.