Check the setup
- Python 3.10 or newer
- A notebook environment such as Jupyter or Google Colab
- An internet connection
Daily CVE exploit-probability scores for building patch-priority queues that go beyond severity ratings and known-exploited lists.
From source to product signal
EPSS estimates the probability that a published CVE will be exploited in the next 30 days. Start with one CVE and the current daily score. EPSS is a likelihood model, not proof of exploitation, and it does not replace asset exposure or CISA Known Exploited Vulnerabilities evidence.
Install the packages, then run the notebook cell.
python -m pip install pandas requests
import pandas as pd
import requests
response = requests.get(
"https://api.first.org/data/v1/epss",
params={"cve": "CVE-2024-3400"},
timeout=30,
)
response.raise_for_status()
scores = pd.DataFrame(response.json()["data"])
scores["retrieved_at_utc"] = pd.Timestamp.now(tz="UTC")
print(scores[["cve", "epss", "percentile", "date"]].head())Test a useful signal
Test whether EPSS can rank a published CVE before treating it as an active incident.
Forum of Incident Response and Security Teams is a nonprofit source. Last verified 2026-08-18. Temporal coverage: 2021-present.