{"id":56075,"date":"2025-06-30T09:39:17","date_gmt":"2025-06-30T09:39:17","guid":{"rendered":""},"modified":"2025-09-26T20:58:48","modified_gmt":"2025-09-27T02:58:48","slug":"cve-2025-52566-heap-overflow-vulnerability-in-llama-cpp-s-tokenizer-implementation","status":"publish","type":"post","link":"https:\/\/www.ameeba.com\/blog\/cve-2025-52566-heap-overflow-vulnerability-in-llama-cpp-s-tokenizer-implementation\/","title":{"rendered":"CVE-2025-52566: Heap Overflow Vulnerability in llama.cpp’s Tokenizer Implementation<\/strong>"},"content":{"rendered":"

Overview<\/strong><\/p>\n

The Common Vulnerabilities and Exposures (CVE) system has recently reported a severe vulnerability, CVE-2025-52566. This vulnerability is found in the llama.cpp inference engine used in several Low-Level Abstract Machine (LLM) models coded in C\/C++. It poses a significant risk to systems utilizing these models, potentially leading to full system<\/a> compromise or data leakage.
\nThe vulnerability is particularly concerning due to its high severity<\/a> score (CVSS 8.6) and the potential widespread impact it could have. This is due to the widely employed LLM models in various applications<\/a> across various industries. Any exploit could result in severe consequences, making it critical for users<\/a> to understand and mitigate this risk promptly.<\/p>\n

Vulnerability Summary<\/strong><\/p>\n

CVE ID: CVE-2025-52566
\nSeverity: High (CVSS score 8.6)
\nAttack Vector: Local Network
\nPrivileges Required: Low
\nUser Interaction: None
\nImpact: Potential system<\/a> compromise or data leakage<\/p>\n

Affected Products<\/strong><\/p>\n

Product | Affected Versions<\/p>\n

LLM models | Prior to version<\/a> b5721<\/p>\n

How the Exploit Works<\/strong><\/p>\n

The CVE-2025-52566 vulnerability is a heap overflow<\/a> vulnerability in the llama.cpp’s tokenizer implementation. This vulnerability stems from a signed vs. unsigned integer overflow<\/a> in the token copying size comparison process. An attacker can exploit this vulnerability by providing a specially crafted text input<\/a> during the tokenization process.
\nThis situation results in the llama.cpp inference engine overflowing<\/a>, which in turn leads to unintended and potentially harmful behavior. The heap overflow allows for the execution of arbitrary code<\/a>, which can compromise the system or lead to data leakage.<\/p>\n

Conceptual Example Code<\/strong><\/p>\n

To illustrate, consider the following conceptual example of how the vulnerability might be exploited. This example assumes the attacker has access to the local network and can provide malicious input to the tokenization<\/a> process.<\/p>\n

#include \"llama_vocab.h\"\nint main() {\nllama_vocab vocab;\nstd::string malicious_input = \"specially crafted text causing overflow...\";\n\/\/ Trigger the overflow vulnerability\nvocab.tokenize(malicious_input);\nreturn 0;\n}<\/code><\/pre>\n
In this example, the `malicious_input` string is designed to cause an integer overflow<\/a> in the tokenization process, leading to a heap overflow. This could potentially allow the attacker to execute arbitrary code<\/a> or cause detrimental system behaviors.<\/p>\n
Recommendations for Mitigation<\/strong><\/p>\n
To mitigate this vulnerability<\/a>, users should apply the patch provided by the vendor, which is available in version b5721 and later of the LLM models. Additionally, the deployment of a Web<\/a> Application Firewall (WAF) or an Intrusion Detection System (IDS) can provide temporary mitigation until the patch can be applied. Furthermore, it’s advised to adhere to best practices such as limiting system privileges<\/a> and monitoring system behavior for unusual activities.<\/p>\n","protected":false},"excerpt":{"rendered":"
Overview The Common Vulnerabilities and Exposures (CVE) system has recently reported a severe vulnerability, CVE-2025-52566. This vulnerability is found in the llama.cpp inference engine used in several Low-Level Abstract Machine (LLM) models coded in C\/C++. It poses a significant risk to systems utilizing these models, potentially leading to full system compromise or data leakage. The […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[1],"tags":[],"vendor":[],"product":[],"attack_vector":[86],"asset_type":[],"severity":[],"exploit_status":[],"class_list":["post-56075","post","type-post","status-publish","format-standard","hentry","category-uncategorized","attack_vector-buffer-overflow"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t