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CVE-2026-18503 Python Software Foundation CVE debrief

This CVE debrief is based on the supplied source corpus. The CVE record was published on 2026-08-10T14:17:21.560Z and has not been modified since then. The vulnerability affects Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function. Defenders should assess exposure and verify the vulnerability status of their applications to prevent potential CPU consumption issues and performance impacts. The CVE record and source references indicate that attacker-controlled CSV samples can trigger super-linear regular-expression work during dialect sniffing and consume significant CPU when applications pass unbounded input to csv.Sniffer.sniff().

Vendor
Python Software Foundation
Product
CPython
CVSS
LOW 2.4
CISA KEV
Not listed in stored evidence
Original CVE published
2026-08-10
Original CVE updated
2026-10-14
Advisory published
2026-08-10
Advisory updated
2026-10-14

Who should care

Defenders responsible for Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function, should assess exposure and verify the vulnerability status of their applications.

Why it matters

Defenders should prioritize verifying the vulnerability status of Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function, to prevent potential CPU consumption issues and performance impacts.

  • Potential CPU consumption issues due to super-linear regular-expression work during dialect sniffing.
  • Possible performance impact on Python applications that handle CSV input.

Technical summary

Attacker-controlled CSV samples can trigger super-linear regular-expression work during dialect sniffing and consume significant CPU when applications pass unbounded input to csv.Sniffer.sniff(). The vulnerability affects Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function. This can lead to potential CPU consumption issues and performance impacts on Python applications that handle CSV input. Defenders should prioritize verifying the vulnerability status of Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function.

Defensive priority

Defenders should prioritize verifying the vulnerability status of Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function.

Recommended defensive actions

  • Verify the vulnerability status of Python applications that handle CSV input, especially those that use the csv.Sniffer.sniff() function.
  • Update Python applications to use bounded input when passing data to csv.Sniffer.sniff().
  • Monitor Python application performance for potential CPU consumption issues related to CSV input.
  • 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 source references indicate that attacker-controlled CSV samples can trigger super-linear regular-expression work during dialect sniffing and consume significant CPU when applications pass unbounded input to csv.Sniffer.sniff().

Sources and references

Verified primary and authoritative sources

  • CVE-2026-18503 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-18503

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

  • CVE-2026-18503 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-18503

    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.