CVE-2026-77014 Red Hat CVE debrief

Who should care

Users of libsoup, particularly those serving large resources over HTTP, should be aware of this vulnerability and take steps to mitigate potential data integrity impacts. Operators of platforms using libsoup, vulnerability management teams, and security teams should review the vulnerability's impact on their environments and prioritize remediation efforts. Affected deployments may require immediate attention to prevent potential data integrity issues. Compensating controls such as monitoring and exception tracking should be implemented for exposed systems while remediation is scheduled and verified. Asset inventory checks are necessary to identify and prioritize affected systems. Security teams should track exceptions, retest remediated assets, and close the item only after evidence is documented. This vulnerability may require updates to incident response plans and communication with stakeholders about potential data integrity impacts. The vulnerability's impact on data integrity and availability should be carefully assessed, and defensive actions should be prioritized accordingly. Security teams should also consider implementing additional monitoring and logging to detect potential exploitation attempts. Furthermore, the vulnerability's impact on compliance and regulatory requirements should be evaluated, and necessary steps should be taken to ensure compliance. The implementation of compensating controls and mitigation strategies should be coordinated with relevant stakeholders, including IT teams, security teams, and management. The vulnerability's impact on business operations and reputation should be carefully assessed, and necessary steps should be taken to minimize potential damage. The vulnerability's impact on customer data and confidentiality should be evaluated, and necessary steps should be taken to protect customer data. The implementation of security patches and updates should be prioritized, and necessary steps should be taken to ensure timely remediation of affected systems. The vulnerability's impact on third-party dependencies and supply chain should be evaluated, and necessary steps should be taken to mitigate potential risks. The security of

Technical summary

The sort_ranges() comparator in soup-message-headers.c truncates a 64-bit subtraction result to 32-bit int, flipping the sign for range offsets differing by more than INT_MAX. This causes silent omission of requested byte ranges from HTTP 206 Partial Content responses on resources larger than approximately 2 GB. The issue affects libsoup's SoupServer HTTP Range header processing, potentially impacting data integrity for users serving large resources over HTTP.

Defensive priority

Medium-priority defensive actions are recommended due to the CVSS score of 5.3 and the potential for data integrity impacts.

Recommended defensive actions

• Review and apply patches or updates from the vendor when available.
• Implement compensating controls such as monitoring and exception tracking for potential data integrity impacts.
• Conduct inventory checks to identify affected systems and prioritize remediation efforts.
• Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up.
• Review the supplied official advisory or CVE record to validate affected scope, severity, and vendor guidance.
• Plan vendor-supported updates or mitigations through normal change control where exposure is confirmed.
• Check relevant monitoring, detection, and logs for exposed assets that need extra review.

Evidence notes

Evidence is based on limited details from the NVD and a few source references. Further investigation is needed to fully understand the vulnerability's impact and affected systems. The vulnerability affects libsoup, particularly in its SoupServer HTTP Range header processing, which may lead to silent omission of requested byte ranges in HTTP 206 Partial Content responses for resources larger than approximately 2 GB. Users should verify their deployments and review compensating controls.

Sources and 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.