hostap: enrich fuzzing seed corpora#66
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| if isinstance(data, str): | ||
| data = data.encode() | ||
| with open(os.path.join(corpus_dir, name), "wb") as f: | ||
| f.write(data) |
Fuzzing Coverage ReportTested: project
Per-harness
Δ = (after − before) / before, to accommodate that denominators may change. "new" when before is 0; "deleted" when after is 0. |
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What
hostap ships 19 in-tree libFuzzer targets under
tests/fuzzing/(X.509/ASN.1,TLS, EAP-SIM/AKA/MSCHAPv2, EAPOL, RADIUS, SAE, P2P/WNM/PASN, 802.11 mgmt).
Each target only ships a 1–5 input example corpus in
tests/fuzzing/*/corpus/, so a run spends most of its budget rediscoveringbasic structure before reaching the parsers of interest.
This change enriches the seed corpora at build time, without modifying any
harness or upstream source:
seeds/gen_seeds.py(new): emits structured seeds assembled field-by-fieldfrom the relevant RFC / IEEE 802.11 / hostap wire formats (JSON, DPP URI,
RADIUS, SAE commit, framed EAP-SIM/AKA/MSCHAPv2, EAPOL, P2P/WNM IEs, full
802.11 management-frame exchanges).
build.sh: runs the generator, and converts the X.509 certificates, CSRs,CRLs and PKCS#8/bind9: fix broken build #12 keys already present in
tests/hwsim/auth_serv/to DER,adding them to the
x509/asn1corpora.Dockerfile:COPY seeds \$SRC/seeds.Existing example inputs are preserved; only generated/derived inputs are
added. No binary blobs are committed — every seed is produced from readable
code or from material already in the tree.
Measured impact
Baseline coverage for the existing harness set is tracked by Fuzz
Introspector (long-running production fuzzing):
https://introspector.oss-fuzz.com/project-profile?project=hostap — 18.30%
line coverage (25,218 / 137,773).
Measured locally on a clean
build_fuzzers(no local source):rises from `cov: 17` (2 upstream example inputs) to `cov: 199` using only
the in-tree certificates converted to DER — and higher with the generated
seeds added.
enriched corpora, reaches 16.8% aggregate line coverage
(23,118 / 137,810) — i.e. ~92% of the long-running production figure is
reached within five minutes from a cold start, versus a substantially
lower figure with the upstream example corpora alone.
This is a corpus-quality improvement: it lets the existing targets reach
deep parser state quickly. It does not change the reachable surface, so it
does not raise the long-run production ceiling; it shortens the time to
reach it.
Testing
`python3 infra/helper.py build_fuzzers hostap` succeeds on a clean clone and
produces the enriched `*_seed_corpus.zip` for every target. A 5-minute
`run_fuzzer` pass over all 19 targets completed with no crashes.