CVE-2024-23496: Llama.cpp Buffer Overflow Vulnerability

CVE-2024-23496 Overview

A heap-based buffer overflow vulnerability exists in the GGUF library gguf_fread_str functionality of llama.cpp. This vulnerability, identified in Commit 18c2e17, allows attackers to achieve code execution through a specially crafted .gguf file. The flaw resides in how the library processes string data when reading GGUF model files, enabling memory corruption that can be leveraged for arbitrary code execution.

Critical Impact

Remote code execution is possible when a user loads a malicious .gguf model file, potentially compromising systems running llama.cpp-based AI applications without any authentication requirements.

Affected Products

• ggerganov llama.cpp (Commit 18c2e17 and potentially adjacent versions)

Discovery Timeline

• 2024-02-26 - CVE-2024-23496 published to NVD
• 2025-11-04 - Last updated in NVD database

Technical Details for CVE-2024-23496

Vulnerability Analysis

This vulnerability combines two critical weakness types: Integer Overflow (CWE-190) and Out-of-Bounds Write (CWE-787). The gguf_fread_str function in the GGUF library fails to properly validate string length values read from .gguf files before allocating heap memory and copying data. When processing a maliciously crafted file, an integer overflow can occur during size calculations, resulting in a smaller-than-expected buffer allocation. Subsequent data copying operations then overflow this undersized buffer, corrupting adjacent heap memory.

The network attack vector with no authentication requirements means that any application accepting .gguf files from untrusted sources—such as model sharing platforms or user uploads—is potentially vulnerable. The exploitation requires no user interaction beyond loading the malicious file, making it particularly dangerous in automated model processing pipelines.

Root Cause

The root cause is improper validation of string length fields within the GGUF file format parser. The gguf_fread_str function reads a length value from the file and uses it directly in memory allocation and copy operations without adequate bounds checking. An attacker can craft a .gguf file with a length field that causes an integer overflow when calculating the required buffer size, leading to a heap buffer that is too small for the actual data being read.

Attack Vector

The attack is executed by providing a malicious .gguf file to any application or service built on llama.cpp that processes model files. The attack flow involves:

1. The attacker crafts a .gguf file containing a malformed string length field designed to trigger an integer overflow
2. When the victim's application loads this file, the gguf_fread_str function allocates an undersized heap buffer
3. The subsequent file read operation copies more data than the buffer can hold, overwriting adjacent heap metadata and data structures
4. By carefully controlling the overflow data, the attacker can corrupt function pointers or other critical data to redirect execution flow and achieve arbitrary code execution

The vulnerability is described in detail in the Talos Intelligence Vulnerability Report.

Detection Methods for CVE-2024-23496

Indicators of Compromise

• Unexpected crashes or segmentation faults in llama.cpp-based applications when loading .gguf files
• Anomalous heap memory patterns or corruption detected by memory sanitizers
• Suspicious .gguf files with abnormal string length fields or unusually large metadata sections

Detection Strategies

• Implement file integrity monitoring for .gguf files entering the environment, comparing against known-good model hashes
• Deploy memory corruption detection tools (AddressSanitizer, Valgrind) in development and staging environments to catch exploitation attempts
• Monitor for unusual process behavior following model file loading, including unexpected child processes or network connections
• Use application-level logging to track all .gguf file loading events with source attribution

Monitoring Recommendations

• Enable crash dump collection and analysis for llama.cpp processes to identify potential exploitation attempts
• Implement behavioral monitoring for processes that load .gguf files, alerting on anomalous post-load activities
• Review and audit the provenance of all .gguf model files before deployment in production environments

How to Mitigate CVE-2024-23496

Immediate Actions Required

• Update llama.cpp to a version released after Commit 18c2e17 that includes the security fix
• Restrict processing of .gguf files to trusted sources only until patching is complete
• Implement network segmentation to isolate systems running vulnerable llama.cpp instances
• Review access controls for any services that accept user-supplied model files

Patch Information

Organizations should update llama.cpp to the latest available version from the official repository. The vulnerability was identified in Commit 18c2e17, so any commits addressing this issue will be subsequent to this version. Check the Talos Intelligence Advisory for specific patch commit references and additional remediation guidance.

Workarounds

• Implement strict input validation at the application layer to reject .gguf files with suspicious characteristics before passing them to the library
• Run llama.cpp applications in sandboxed environments (containers, VMs) with minimal privileges to limit the impact of successful exploitation
• Deploy application-level firewalls or content filtering to scan uploaded model files before processing
• Consider compiling llama.cpp with memory hardening options such as stack canaries, ASLR, and heap protection mechanisms