CVE-2026-59250 Erlang CVE debrief

Who should care

Organizations using Erlang/OTP, particularly versions prior to OTP 29.0.4, OTP 28.5.0.4, and OTP 27.3.4.15, should be aware of this vulnerability and take immediate action to mitigate the risk. This includes assessing their exposure, applying patches or updates, and implementing compensating controls. Additionally, security teams and vulnerability management teams should prioritize this vulnerability due to its high severity and potential impact.

Technical summary

The vulnerability occurs in the flex scanner before any grammar or Megaco-level authentication processing. An attacker can send a specially crafted H.248/Megaco message to corrupt the driver's memory, potentially achieving remote code execution or a denial-of-service crash. The overflow occurs in the mfs_load_property_groups function, which extracts the attacker-controlled property name and formats it into a fixed 512-byte error_msg field using an unchecked sprintf call. This issue affects OTP from OTP 17.0 before OTP 29.0.4, OTP 28.5.0.4 and OTP 27.3.4.15, corresponding to megaco from 3.17.1 before 4.9.1, 4.8.3.1 and 4.7.2.2.

Defensive priority

High

Recommended defensive actions

• Inventory and assess Erlang/OTP installations for potential exposure
• Apply patches or updates to vulnerable versions (OTP 29.0.4, OTP 28.5.0.4, OTP 27.3.4.15)
• Implement compensating controls, such as network segmentation and access controls
• Monitor for suspicious activity and anomalous behavior
• Consider disabling the megaco transport port on nodes not requiring it
• Review and update incident response plans to address potential exploitation
• Conduct a thorough risk assessment to identify and prioritize affected systems

Evidence notes

The CVE record was published on 2026-07-27T16:18:03.330Z and has not been modified since then. The NVD entry is currently Received. This information is based on the supplied source corpus and has not been independently verified. Defenders should verify the accuracy of this information with official sources and consider the potential impact on their systems.

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.