CVE-2026-27940: llama.cpp Buffer Overflow Vulnerability

CVE-2026-27940 Overview

CVE-2026-27940 is an Integer Overflow vulnerability in llama.cpp, the popular open-source C/C++ implementation for LLM (Large Language Model) inference. The vulnerability exists in the gguf_init_from_file_impl() function within gguf.cpp, where an integer overflow leads to an undersized heap allocation. This flaw allows attackers to write 528+ bytes of attacker-controlled data past the buffer boundary using a subsequent fread() operation. Notably, this vulnerability is a bypass of a similar previously patched bug (CVE-2025-53630), indicating that the original fix was incomplete.

Critical Impact

Successful exploitation could allow local attackers to achieve arbitrary code execution by corrupting heap memory with attacker-controlled data, potentially compromising systems running LLM inference workloads.

Affected Products

• llama.cpp versions prior to b8146
• Applications and services built using vulnerable llama.cpp libraries
• AI/ML inference deployments utilizing unpatched llama.cpp implementations

Discovery Timeline

• 2026-03-12 - CVE-2026-27940 published to NVD
• 2026-03-12 - Last updated in NVD database

Technical Details for CVE-2026-27940

Vulnerability Analysis

This vulnerability stems from improper integer handling in the GGUF file parsing functionality of llama.cpp. The gguf_init_from_file_impl() function fails to properly validate arithmetic operations when calculating buffer sizes, allowing an integer overflow condition to occur. When the overflow happens, the resulting allocation size is significantly smaller than intended, creating an undersized heap buffer.

The subsequent fread() operation then writes data from a maliciously crafted GGUF file directly into this undersized buffer, resulting in a heap buffer overflow. The attacker can control the overflow data, writing 528 or more bytes beyond the allocated boundary. This type of heap corruption primitive is particularly dangerous as it can be leveraged for arbitrary code execution through various heap exploitation techniques.

This vulnerability is classified as CWE-122 (Heap-based Buffer Overflow) and represents an incomplete fix bypass of CVE-2025-53630, which addressed similar issues in the same file but failed to account for all vulnerable code paths.

Root Cause

The root cause is insufficient integer overflow checking in the gguf_init_from_file_impl() function when calculating allocation sizes from values parsed from GGUF model files. The original fix for CVE-2025-53630 addressed some instances of this pattern but overlooked additional locations where the same vulnerability pattern existed. GGUF files can contain attacker-controlled values that, when used in size calculations, trigger integer overflow conditions that bypass the existing bounds checking logic.

Attack Vector

The attack vector requires local access where an attacker must convince a user to load a maliciously crafted GGUF model file or place a malicious file in a location where it will be automatically loaded by an application using llama.cpp. The exploitation process involves:

1. Crafting a malicious GGUF file with specific field values designed to trigger integer overflow during size calculation
2. The victim application loads the malicious GGUF file using the vulnerable gguf_init_from_file_impl() function
3. Integer overflow occurs, resulting in an undersized heap allocation
4. The fread() operation writes 528+ bytes of attacker-controlled content past the buffer boundary
5. The heap corruption can be leveraged to achieve code execution through heap memory manipulation techniques

The vulnerability mechanism involves integer overflow in buffer size calculations during GGUF file parsing. When a maliciously crafted GGUF file is processed, the arithmetic operation for determining allocation size wraps around, resulting in a small allocation. The subsequent file read operation then writes beyond this buffer's boundaries. For detailed technical analysis, see the GitHub Security Advisory.

Detection Methods for CVE-2026-27940

Indicators of Compromise

• Unusual crashes or segmentation faults in llama.cpp-based applications when loading model files
• Unexpected heap corruption errors or memory allocation failures during GGUF file parsing
• Anomalous process behavior following the loading of untrusted GGUF model files

Detection Strategies

• Monitor for application crashes with heap corruption signatures in llama.cpp processes
• Implement file integrity monitoring for GGUF model files in production environments
• Use memory sanitizers (AddressSanitizer, Valgrind) during development to detect heap overflows
• Employ endpoint detection solutions capable of identifying heap spray and corruption attack patterns

Monitoring Recommendations

• Enable detailed logging for GGUF file loading operations in production deployments
• Configure crash dump collection for llama.cpp applications to aid in forensic analysis
• Monitor system calls related to file operations and memory allocations in LLM inference processes

How to Mitigate CVE-2026-27940

Immediate Actions Required

• Upgrade llama.cpp to version b8146 or later immediately
• Audit all deployed applications and services using llama.cpp for vulnerable versions
• Restrict access to GGUF model file directories to trusted users only
• Implement strict input validation for any externally sourced model files

Patch Information

The vulnerability is fixed in llama.cpp version b8146. Organizations should update their llama.cpp installations to this version or later. The fix addresses the incomplete patch from CVE-2025-53630 by implementing proper integer overflow checks across all affected code paths in the gguf_init_from_file_impl() function. For patch details and release information, refer to the GitHub Security Advisory.

Workarounds

• Only load GGUF model files from trusted and verified sources
• Implement application-level sandboxing to limit the impact of potential exploitation
• Use containerization to isolate llama.cpp inference workloads from critical systems
• Consider implementing file hash verification for all model files before loading

# Configuration example
# Verify llama.cpp version to ensure patched version is installed
git -C /path/to/llama.cpp describe --tags
# Expected output should show b8146 or later

# Restrict permissions on model directories
chmod 750 /path/to/models
chown root:trusted-users /path/to/models