Files
colibri/c/tools
JustVugg 4268e00fa1 security: harden safetensors/JSON parsers against malicious model files
A downloaded (supply-chain) model file was fully trusted by the loader. Three
memory-safety holes, all reachable by pointing the engine at a crafted shard —
demonstrated crashing on pre-fix, now rejected fail-closed:

st.h (safetensors):
- header length `hlen` (u64 from the file) was unbounded before malloc(hlen+1):
  a crafted value overflows (malloc(0) then hdr[hlen]=0 OOB) or forces a giant
  allocation. Now bounded to the file size and a 512 MB cap; malloc NULL-checked.
- json_get() returns NULL for missing/mistyped fields, but dtype/data_offsets/
  shape were dereferenced blind (off->kids[0]) — a header omitting data_offsets
  SIGSEGV'd (verified). Now type/arity-checked before use.
- data_offsets [a0,b0] were trusted: b0<a0 gave a negative nbytes -> malloc((size_t))
  giant and an oversized memcpy into the caller's buffer in st_read_f32 (heap
  overflow); off could point outside the file. Now validated 0<=a0<=b0 and
  data_start+b0<=filesize.

json.h: j_parse_val recursed with no depth limit -> stack overflow on nested
input like [[[[...]]]]. Added J_MAX_DEPTH=1024 (headers are ~3 deep); wide-but-
flat objects like the GLM header are unaffected (depth is decremented per return).

eval_glm.py: tempfile.mktemp() -> mkstemp() — closes the TOCTOU/symlink race on
a shared tmp dir (CWE-377).

Network path (openai_server.py + serve SUBMIT parser) audited separately and is
already sound: hmac.compare_digest auth, MAX_BODY cap, resolve()+relative_to
traversal guard, list-form subprocess, bounded/validated SUBMIT header. All 62
tests pass; valid GLM/OLMoE shards load unchanged.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-15 08:13:20 +02:00
..

Tools

These scripts support model preparation and offline engineering work. They are not runtime dependencies of the C engine.

  • convert_fp8_to_int4.py, download_glm52.py: model preparation
  • make_glm_oracle.py, make_glm_bench_model.py: deterministic fixtures
  • benchmark_cuda_fixture.py, eval_glm.py, fetch_benchmarks.py: benchmarks
  • gen_unicode.py: tokenizer table generation

Run them from c/, for example:

python3 tools/convert_fp8_to_int4.py --selftest
python3 tools/make_glm_bench_model.py --output /tmp/colibri-bench