cvss-estimator

Two things in one small, dependency-free package:

1. A correct CVSS v3.1 base-score calculator — build, parse, and score vectors per the official specification arithmetic.
2. An evidence-driven estimator — turn source-to-sink evidence flags (is the attacker path complete? is there a sanitizer? a public entrypoint?) plus a CWE/summary into a plausible, explainable CVSS vector and score. Useful for generating a stable machine baseline before a human refines it.

Install

pip install cvss-estimator

Scoring an existing vector

from cvss_estimator import cvss_base_score, parse_vector, severity

metrics = parse_vector("CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H")
score = cvss_base_score(metrics)   # 9.8
print(severity(score))             # "Critical"

cvss-estimator score "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H"

Estimating a vector from evidence

from cvss_estimator import estimate

result = estimate(
    cwe="CWE-502",  # unsafe deserialization -> RCE category
    evidence={
        "attacker_path_complete": True,   # full source-to-sink -> AC:L, AV:N
        "entrypoint_found": True,
        "sanitizer_or_allowlist_present": False,
    },
)
print(result["vector"])    # CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
print(result["score"])     # 9.8
print(result["severity"])  # Critical

cvss-estimator estimate --cwe CWE-502 --complete-path --entrypoint http_route

Evidence flags

Categories are inferred from CWE/summary via impact_profile(): remote_code_execution, sql_injection, ssrf, xxe, path_traversal, secret_exposure, denial_of_service, security_weakness.

Notes

• No runtime dependencies.
• The estimator is a heuristic baseline, not an authoritative score — always have a human confirm the vector before publishing a CVE.

License

MIT