CVE-2025-53630 is a high-severity heap-based buffer overflow vulnerability identified in the ggml-org's llama.cpp project, which is a C/C++ implementation for inference of several large language models (LLMs). The root cause of the vulnerability lies in an integer overflow within the function gguf_init_from_file_impl located in the source file ggml/src/gguf.cpp. This integer overflow can lead to heap out-of-bounds read and write operations, which may allow an attacker to corrupt memory, potentially leading to arbitrary code execution, application crashes, or data leakage. The vulnerability affects all versions of llama.cpp prior to the commit 26a48ad699d50b6268900062661bd22f3e792579, where the issue was fixed. The CVSS v4.0 base score is 8.9, indicating a high severity with network attack vector, no required privileges or user interaction, and high impact on confidentiality, integrity, and availability. The vulnerability does not require authentication or user interaction, making it exploitable remotely if the vulnerable llama.cpp implementation is exposed to untrusted inputs. While no known exploits are currently reported in the wild, the potential for exploitation is significant due to the nature of the vulnerability and the increasing adoption of LLM inference engines in various applications. The CWE identifiers associated are CWE-122 (Heap-based Buffer Overflow) and CWE-680 (Integer Overflow to Buffer Overflow), highlighting the technical cause and effect of the flaw. This vulnerability is critical for any deployment using llama.cpp for LLM inference, especially in environments where untrusted data is processed or where the inference engine is exposed to network inputs.

For European organizations, the impact of CVE-2025-53630 can be substantial, particularly for those integrating llama.cpp into their AI and machine learning workflows, including research institutions, technology companies, and enterprises deploying AI-powered services. Exploitation could lead to unauthorized code execution, data breaches, or denial of service, compromising sensitive data and disrupting critical AI services. Given the growing reliance on LLMs for natural language processing, automation, and decision-making, a successful attack could undermine trust in AI systems and cause operational downtime. Furthermore, organizations in regulated sectors such as finance, healthcare, and government may face compliance violations and reputational damage if this vulnerability is exploited. The vulnerability’s remote exploitability and lack of required privileges increase the risk profile, especially if llama.cpp is integrated into publicly accessible services or cloud environments. Additionally, the potential for lateral movement within networks after initial compromise could amplify the damage. European organizations must consider the threat in the context of increasing cyber espionage and ransomware activities targeting AI infrastructure.

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.