Use pytricia for ip matching on non-windows machines #547
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bitterpanda63
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| def test_with_ranges(): | ||
| input_list = [ | ||
| "192.168.0.0/24", |
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Maybe good to test with multiple overlapping ranges too?
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these test cases are from node iirc? (also a bit out of scope, since this was existing code copied over)
| matcher = IPMatcher(input_list) | ||
| assert matcher.has("::ffff:0.0.0.0") == True | ||
| assert matcher.has("::ffff:127.0.0.1") == True | ||
| assert matcher.has("::ffff:123") == False |
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so this case is different for both?
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yes, PyTricia is more accurate in this regard, this is also not a huge issue, and I think no one is running with windows in production. Given IPC also doesnt work
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remove ipv4-mapped ipv6 addresses being added manually in imds.py
| # Block the IP address used by Alibaba Cloud | ||
| imds_addresses.add("100.100.100.200") | ||
| imds_addresses.add(map_ipv4_to_ipv6("100.100.100.200")) | ||
| imds_addresses = IPMatcher( |
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imds_addresses is a module-level IPMatcher initialized at import; it will persist across requests and may unintentionally cache data between requests. Consider making it request-scoped or explicitly documenting intended shared cache.
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✨ AI Reasoning
A module-level variable is being initialized during import and stored for the process lifetime. This can unintentionally cache request- or environment-specific data between requests/workers, causing cross-request leakage or stale data. The change replaces the prior empty-initialization line with a populated IPMatcher created at module import, increasing the chance that runtime state is shared across requests.
🔧 How do I fix it?
Avoid storing request-specific data in module-level variables. Use request-scoped variables or explicitly mark shared caches as intentional.
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Reply @AikidoSec ignore: [REASON] to ignore this issue.
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Summary by Aikido
🚀 New Features
⚡ Enhancements
🔧 Refactors
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