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python-zeroconf: Unbounded TC-deferred queue allows LAN-local memory exhaustion via spoofed-source flood

Moderate severity GitHub Reviewed Published May 21, 2026 in python-zeroconf/python-zeroconf • Updated Jun 11, 2026

Package

pip zeroconf (pip)

Affected versions

< 0.149.12

Patched versions

0.149.12

Description

Impact

AsyncListener.handle_query_or_defer retained every truncated (TC-bit) incoming query in self._deferred[addr] and armed a per-addr timer in self._timers[addr] that flushed the reassembled query within ~500 ms (RFC 6762 §18.5). Neither the per-addr list nor the number of distinct addr keys was capped, and the dedup check (for incoming in reversed(deferred): if incoming.data == msg.data) ran O(N) over the per-addr list on every arrival.

Any unauthenticated host on the local link (UDP/5353, 224.0.0.251 / ff02::fb) can stream byte-distinct TC-flagged mDNS queries — each up to _MAX_MSG_ABSOLUTE = 8966 bytes, with DNSIncoming retaining the raw data buffer plus parsed-record state. Trivially spoofed source IPs multiply the effect across _deferred / _timers, and the O(N) data compare burns CPU quadratically as each per-addr queue grows. On memory-constrained deployments (Home Assistant on Raspberry-Pi-class hardware is the canonical victim) sustained traffic OOM-kills the process; under lighter load, the per-arrival scan and event-loop scheduler starvation break unrelated zeroconf consumers (discovery, registration, ServiceBrowser callbacks).

Patches

Fixed in zeroconf 0.149.12 (PR #1751). Upgrade to >= 0.149.12.

Workarounds

There is no in-process workaround; upgrading is the fix. Otherwise, restrict mDNS (UDP/5353) to trusted Layer-2 segments via AP client isolation, guest-network separation, or host firewall rules.

Resources

References

@bdraco bdraco published to python-zeroconf/python-zeroconf May 21, 2026
Published to the GitHub Advisory Database Jun 11, 2026
Reviewed Jun 11, 2026
Last updated Jun 11, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Adjacent
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(5th percentile)

Weaknesses

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource. Learn more on MITRE.

Allocation of Resources Without Limits or Throttling

The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated. Learn more on MITRE.

CVE ID

CVE-2026-48045

GHSA ID

GHSA-9663-mqmp-p9mm
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