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CVE-2026-82617 Apache Software Foundation CVE debrief

CVE-2026-82617 is a critical vulnerability in Apache OpenNLP, affecting versions from 2.0.0 through 2.5.11 and 3.0.0-M1 through 3.0.0-M5. The issue arises from ambiguous nested quantifiers in the DEFAULT_REGEX_NAME_FINDER.EMAIL and DEFAULT_REGEX_NAME_FINDER.URL patterns, which can lead to super-linear backtracking or unbounded matcher recursion when processing untrusted text. This can be exploited to cause denial-of-service (DoS) attacks, consuming excessive CPU or causing thread stack overflows.

Vendor
Apache Software Foundation
Product
Apache OpenNLP
CVSS
CRITICAL 10
CISA KEV
Not listed in stored evidence
Original CVE published
2026-09-11
Original CVE updated
2026-09-11
Advisory published
2026-09-11
Advisory updated
2026-09-11

Who should care

Defenders and developers using Apache OpenNLP should assess exposure and apply patches immediately to prevent potential DoS attacks. This includes reviewing compensating controls for exposed systems, monitoring for potential attacks, and tracking exceptions and remediated assets. Security teams and vulnerability management teams should prioritize patching and verifying the effectiveness of the patches.

Why it matters

CVE-2026-82617 is a critical vulnerability in Apache OpenNLP that can be exploited for DoS attacks. Defenders and developers should assess exposure and apply patches immediately.

  • Denial-of-service (DoS) attacks through excessive CPU consumption
  • Denial-of-service (DoS) attacks through thread stack overflows
  • Potential for attackers to cause service disruption without authentication

Technical summary

The vulnerability is caused by ambiguous nested quantifiers in the DEFAULT_REGEX_NAME_FINDER.EMAIL and DEFAULT_REGEX_NAME_FINDER.URL patterns in Apache OpenNLP. This can lead to super-linear backtracking or unbounded matcher recursion when processing untrusted text, potentially causing DoS attacks. Affected product deployments should be assessed for exposure, and patches should be applied immediately to prevent potential DoS attacks. The Apache OpenNLP project has released patches for the issue, which fix the vulnerability.

Defensive priority

Upgrade to version 2.5.12 or 3.0.0-M6. Assess exposure and apply patches immediately.

Recommended defensive actions

  • Upgrade to version 2.5.12 or 3.0.0-M6
  • Assess exposure and apply patches immediately
  • Monitor for potential DoS attacks through excessive CPU consumption or thread stack overflows
  • Review compensating controls for exposed systems while remediation is scheduled and verified
  • Check relevant monitoring, detection, and logs for exposed assets that need extra review
  • Track exceptions, retest remediated assets, and close the item only after evidence is documented
  • Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up

Evidence notes

The CVE record and NVD vulnerability detail provide information on the vulnerability. The Apache OpenNLP project has released patches for the issue. Affected product deployments should be assessed for exposure, and patches should be applied immediately to prevent potential DoS attacks. The DEFAULT_REGEX_NAME_FINDER.EMAIL and DEFAULT_REGEX_NAME_FINDER.URL patterns in Apache OpenNLP contain ambiguous nested quantifiers, which can lead to super-linear backtracking or unbounded matcher recursion when processing untrusted text. This can be

Sources and references

Verified primary and authoritative sources

  • CVE-2026-82617 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-82617

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2026-82617 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-82617

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

Supplemental references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.