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Exploit Prediction Scoring System (EPSS)

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arxiv 1908.04856 v1 pith:BJ42INKV submitted 2019-08-13 cs.CR

classification cs.CR
keywords systemscoringvulnerabilitydataenoughepssexploitfirst
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite the massive investments in information security technologies and research over the past decades, the information security industry is still immature. In particular, the prioritization of remediation efforts within vulnerability management programs predominantly relies on a mixture of subjective expert opinion, severity scores, and incomplete data. Compounding the need for prioritization is the increase in the number of vulnerabilities the average enterprise has to remediate. This paper produces the first open, data-driven framework for assessing vulnerability threat, that is, the probability that a vulnerability will be exploited in the wild within the first twelve months after public disclosure. This scoring system has been designed to be simple enough to be implemented by practitioners without specialized tools or software, yet provides accurate estimates of exploitation. Moreover, the implementation is flexible enough that it can be updated as more, and better, data becomes available. We call this system the Exploit Prediction Scoring System, EPSS.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pinning Is Futile: You Need More Than Local Dependency Versioning to Defend against Supply Chain Attacks

    cs.SE 2025-02 conditional novelty 7.0 of 10

    Pinning direct npm dependencies does not meaningfully reduce malicious-update exposure and can increase it in large graphs, while coordinated pinning of a few key packages cuts ecosystem risk far more.

  2. Conductance-Repair Evidence Graphs for Prospective Security Retrieval

    cs.CR 2026-07 conditional novelty 5.5 of 10

    Conductance-repair graphs recover withheld security document–term edges under temporal masks with certificates, raising recall@k while sometimes lowering average precision on small public fixtures.

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