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VulnerabilityPublished

CVE-2026-34159: ggml-org llama.cpp · Memory Buffer Overflow

Affects ggml-org llama.cppSource CVE Database V5

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Not known to be exploited · CVSS 9.8 Critical : upgrade to b8492 as a priority.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 3.1 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
1.6% chance of exploitation in 30 days – higher than 76% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in b8492
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 1 Apr 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: evidence found.

    Public exploit code: GitHub proofs of concept

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), GitHub proofs of concept, the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.

What to do

Upgrade ggml-org llama.cpp to b8492.

Affected software

ProductAffectedFixed in
ggml-orgllama.cppAffected
  • <b8492
Fixed in
  • b8492

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AI-suggested

  1. Immediately upgrade all deployments of llama.cpp to version b8492 or later, which contains the patch for this vulnerability.
  2. Restrict network access to the RPC server port by implementing strict firewall rules and network segmentation to limit exposure only to trusted hosts.
  3. Employ network-level authentication or VPNs to protect RPC endpoints from unauthorized access.
  4. Monitor network traffic for unusual GRAPH_COMPUTE messages or unexpected RPC activity that could indicate exploitation attempts.
  5. Conduct regular code audits and fuzz testing on RPC deserialization routines to detect similar memory safety issues proactively.
  6. If upgrading is not immediately possible, consider disabling or restricting the RPC backend functionality temporarily to reduce risk.
  7. Implement runtime protections such as AddressSanitizer or Control Flow Integrity (CFI) where feasible to detect exploitation attempts.
  8. Maintain up-to-date intrusion detection and prevention systems tuned to detect anomalous memory manipulation patterns.

Description

llama.cpp is an inference of several LLM models in C/C++. Prior to version b8492, the RPC backend's deserialize_tensor() skips all bounds validation when a tensor's buffer field is 0. An unauthenticated attacker can read and write arbitrary process memory via crafted GRAPH_COMPUTE messages. Combined with pointer leaks from ALLOC_BUFFER/BUFFER_GET_BASE, this gives full ASLR bypass and remote code execution. No authentication required, just TCP access to the RPC server port. This issue has been patched in version b8492.

References

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