CVE-2025-53630: ggml-org llama.cpp · Heap-based Buffer Overflow
Affects ggml-org llama.cppSource CVE Database V5
Public exploit available · CVSS 8.9 High.
Data as of 26 Oct 2026, 11:42 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 29% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:PExploitation evidence
Disclosed: evidence found.
CVE published 10 Jul 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: evidence found.
CVSS exploit maturity: proof of concept public
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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), CVSS vector, the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
AI-suggested
To mitigate CVE-2025-53630 effectively, European organizations should: 1) Immediately update llama.cpp to the fixed version including commit 26a48ad699d50b6268900062661bd22f3e792579 or later, ensuring the integer overflow and buffer overflow issues are resolved. 2) Conduct a thorough inventory of all systems and applications using llama.cpp to identify vulnerable instances, including indirect dependencies in AI pipelines. 3) Implement strict input validation and sanitization for any data fed into llama.cpp inference functions to reduce the risk of triggering the overflow. 4) Employ runtime protections such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and heap protection mechanisms to mitigate exploitation impact. 5) Restrict network exposure of services using llama.cpp, applying network segmentation and firewall rules to limit access to trusted sources only. 6) Monitor logs and system behavior for anomalies indicative of exploitation attempts, including unexpected crashes or memory corruption symptoms. 7) Engage in regular security assessments and penetration testing focused on AI infrastructure to detect similar vulnerabilities proactively. 8) Collaborate with AI software vendors and open-source communities to stay informed about patches and emerging threats related to LLM implementations.
Description
llama.cpp is an inference of several LLM models in C/C++. Integer Overflow in the gguf_init_from_file_impl function in ggml/src/gguf.cpp can lead to Heap Out-of-Bounds Read/Write. This vulnerability is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579.