BUG: fix CPU feature env diagnostic buffer overruns - #31905
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Avoid the trailing-space-and-trim pattern for nonexistent CPU features in npy__cpu_check_env. A maximum-length invalid feature name could write the terminating NUL one byte past the end of `nexist`.
ngoldbaum
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Just two very minor nits, otherwise this looks good to me. The increased stack size doesn't matter I think.
I'll probably merge this soon but let's ping @seiko2plus since he originally wrote this code.
Use the maximum accepted environment variable length for the `notsupp` diagnostic buffer in npy__cpu_check_env as repeated unsupported features could overflow the buffer.
Use %zu when formatting var_len, which is a size_t.
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Rebased to address the comments. |
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Thanks @stratakis! |
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BUG: fix CPU feature env diagnostic buffer overruns (#31905)
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PR summary
This is a follow up to gh-30877.
I've had on the back on my mind to verify and check some other issues I saw in the file and finally managed to get to it.
The original PR fixed a buffer overrun in CPU baseline validation. This PR fixes two similar issues in
npy__cpu_check_env.The two issues fixed:
nexiststack buffernotsuppdiagnostic buffer, which was sized fromNPY_WITH_CPU_DISPATCHrather than from the accepted environment variable length.Both fixes avoid the trailing-space-and-trim pattern when building the diagnostic strings.
Noting though that the fix for the
notsuppbuffer increases stack usage innpy__cpu_check_envbecause the buffer is now bounded byNPY__MAX_VAR_LENinstead ofsizeof(NPY_WITH_CPU_DISPATCH) + 1. On my x86_64 laptop,-fstack-usagereports the function growing from 2224 bytes to 3184 bytes, a 960 bytes increase. The function is non-recursive and only runs while parsing CPU feature environment variables during import, however if the size icrease is deemed unacceptable I can change the approach to something like heap allocation, it will require more code changes though.These tests are mainly sanitizer regressions, in non-sanitized builds, the previous out-of-bounds writes might not crash.
AI Disclosure
I used codex with the gpt 5.5 model. Guided to create the tests, (hence the tests are AI generated) and to review the C code changes, also to verify any incompatibilities (where it verified the stack increase) and to create small reproducers with the same logic for me to actually verify the issues exist.