[Aikido] AI Fix for Dangerous use of assert#1543
Open
aikido-autofix[bot] wants to merge 1 commit into
Open
Conversation
|
The latest updates on your projects. Learn more about Vercel for GitHub.
|
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This patch mitigates dangerous use of assert statements across multiple modules in the guardrails package by replacing assert statements with explicit conditional checks that raise AssertionError exceptions. The affected modules include SQLiteTraceHandler.log_validator method (call_tracing/sqlite_trace_handler.py line 169), _deref_schema_path and _jsonschema_to_jsonformer functions (formatters/json_formatter.py lines 20, 76), invariant string prefix validation logic (merge.py lines 71, 115), on_fail function (utils/on_fail.py line 10), and Validator.init method's rail_alias registry validation (validator_base.py lines 160-162). This ensures critical validation logic remains enforced in production environments where Python optimization flags (-O or -OO) may disable assertions.
Aikido used AI to generate this PR.
High confidence: Aikido has a robust set of benchmarks for similar fixes, and they are proven to be effective.