CVE-2026-109364 in Zebra
Summary
by MITRE • 10/12/2026
Zebra 2.3.0 before 6.4.0 contains a protection mechanism failure that allows unauthenticated peers to evade bans because bans and misbehavior scores are keyed on exact IP addresses. Attackers can reconnect from fresh IPv6 addresses in their /64 allocation or flush the 20,000-entry FIFO ban list to restore access for banned peers.
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Analysis
by VulDB Data Team • 10/12/2026
The vulnerability identified in Zebra versions prior to 6.4.0 represents a critical failure in access control mechanisms, specifically within the implementation of anti-abuse and rate-limiting protections. This flaw allows unauthenticated network peers to bypass bans imposed by the system due to misbehavior or policy violations. The root cause lies in the design of the ban logic, which relies exclusively on exact IP address matching for both temporary bans and long-term misbehavior scoring. By not incorporating additional identifiers such as user agents, session tokens, or behavioral fingerprints that persist across connection resets, the security controls are rendered ineffective against determined adversaries who can manipulate their network identity to appear as new entities.
From a technical perspective, this issue manifests in two primary vectors of exploitation. First, attackers operating within IPv6 networks can leverage the standard /64 subnet allocation assigned by most ISPs and cloud providers. Since an attacker typically has access to thousands of unique IP addresses within such a range, they can simply disconnect from their current banned address and immediately reconnect using a fresh, unused IP from the same block. Because Zebra treats each distinct IPv6 address as a separate entity with no history or reputation linkage, the ban is effectively ignored upon reconnection. Second, for scenarios where an attacker cannot easily rotate IPs or wishes to target specific peers more aggressively, they can exploit the finite size of the internal FIFO (First-In-First-Out) ban list. With a capacity limited to 20,000 entries, an adversary can flood this table with numerous benign-looking connections from various IP addresses. Once the list reaches its maximum capacity, older entries are evicted, effectively flushing out previously banned malicious peers and restoring their access privileges without any penalty or cooldown period.
The operational impact of this vulnerability is significant for network stability and security integrity. It enables persistent denial-of-service attacks, resource exhaustion, and unauthorized data exfiltration by allowing bad actors to continuously reconnect despite repeated violations. This undermines the reliability of services relying on Zebra for peer management, potentially leading to degraded performance or complete service unavailability if malicious peers consume excessive bandwidth or processing resources. Furthermore, it compromises the integrity of reputation systems that depend on consistent tracking of misbehaving nodes, as these records can be easily circumvented through IP rotation or list overflow techniques.
This vulnerability aligns with CWE-284 Improper Access Control and CWE-798 Use of Hard-coded Credentials if related to default configurations, but more specifically maps to CWE-601 URL Redirection to Untrusted Site Open Redirect when considering the evasion aspect, though it is best classified under CWE-352 Cross-Site Request Forgery in a broader sense or simply as an Access Control Bypass. In terms of MITRE ATT&CK framework, this behavior corresponds to T1190 Exploit Public-Facing Application and potentially T1078 Valid Accounts if the system relies on implicit trust based on IP reputation rather than explicit authentication. To mitigate these risks, it is imperative that Zebra 6.4.0 or later be deployed immediately. The fix involves implementing robust identity persistence mechanisms such as binding bans to broader network prefixes for IPv6 while applying stricter rate limits per prefix, and increasing the ban list capacity or switching to a more resilient data structure like an LRU cache with configurable eviction policies that do not allow simple overflow attacks. Additionally, integrating behavioral analysis tools that track connection patterns rather than just static IP addresses can further harden the system against evasion attempts.