CVE-2025-53630: llama.cpp Buffer Overflow Vulnerability

CVE-2025-53630 Overview

CVE-2025-53630 is an integer overflow vulnerability in llama.cpp, an open-source C/C++ inference engine for large language models (LLMs). The flaw resides in the gguf_init_from_file_impl function within ggml/src/gguf.cpp. Attackers can trigger an integer overflow during GGUF model file parsing, producing a heap out-of-bounds read or write [CWE-122]. The issue is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579.

Critical Impact

A malicious GGUF model file can corrupt heap memory in any application that loads it through llama.cpp, enabling potential arbitrary code execution and full compromise of confidentiality, integrity, and availability.

Affected Products

• llama.cpp versions prior to commit 26a48ad699d50b6268900062661bd22f3e792579
• Applications and services embedding vulnerable llama.cpp builds for GGUF model loading
• Downstream ggml consumers that invoke gguf_init_from_file_impl on untrusted input

Discovery Timeline

• 2025-07-10 - CVE-2025-53630 published to NVD
• 2026-04-15 - Last updated in NVD database

Technical Details for CVE-2025-53630

Vulnerability Analysis

The vulnerability lives in gguf_init_from_file_impl, the routine that parses GGUF model containers in ggml/src/gguf.cpp. Untrusted size or count values read from the file header are used in arithmetic operations without sufficient bounds checking. When the computed value wraps past the maximum of its integer type, the result becomes a small or otherwise invalid allocation size while the parser continues to read or write the full attacker-controlled length.

This mismatch produces a heap-based out-of-bounds read or write [CWE-122]. The attack is reachable over the network because GGUF files are routinely fetched from model registries, shared between users, or served from hosted inference endpoints.

Root Cause

The root cause is unchecked integer arithmetic on attacker-controlled length and count fields parsed from the GGUF header and metadata sections. Multiplications between element counts and element sizes can overflow size_t or 32-bit width values, yielding undersized buffers that the subsequent copy or read loop overruns.

Attack Vector

An attacker delivers a crafted GGUF file to a target that loads it with a vulnerable llama.cpp build. Delivery paths include malicious Hugging Face uploads, supply-chain compromise of model repositories, model marketplaces, and any application that accepts user-provided model files. No authentication or user interaction is required beyond loading the file. Refer to the GitHub Security Advisory GHSA-vgg9-87g3-85w8 for technical details.

Detection Methods for CVE-2025-53630

Indicators of Compromise

• Unexpected crashes, segmentation faults, or heap corruption errors in processes hosting llama.cpp
• Inference service processes spawning child processes or shells after loading a model file
• GGUF files sourced from unverified third-party repositories or mirrors
• Anomalous outbound network connections from model-loading processes immediately after a load event

Detection Strategies

• Inventory all binaries and containers that link llama.cpp or ggml and verify the commit hash against 26a48ad699d50b6268900062661bd22f3e792579 or later
• Hash and allow-list GGUF model files at ingestion, blocking loads of unknown hashes
• Instrument staging environments with AddressSanitizer (ASan) to catch heap out-of-bounds access during model loading
• Monitor process telemetry for crashes in gguf_init_from_file_impl call paths

Monitoring Recommendations

• Log every GGUF file load with source URL, file hash, and loading user or service identity
• Alert on crash signals (SIGSEGV, SIGABRT) from inference workers and correlate with recent model fetches
• Track network egress from inference hosts to detect post-exploitation command-and-control activity

How to Mitigate CVE-2025-53630

Immediate Actions Required

• Upgrade llama.cpp to a build that includes commit 26a48ad699d50b6268900062661bd22f3e792579 or later
• Rebuild and redeploy any container images, Python wheels, or downstream packages that vendor llama.cpp
• Restrict GGUF model loading to files from trusted, signed sources only
• Audit existing model repositories for files of unknown provenance and quarantine them pending review

Patch Information

The vulnerability is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579 of the upstream repository. See the GitHub commit and the GHSA-vgg9-87g3-85w8 advisory for fix details. Rebuild all downstream binaries against a patched source tree.

Workarounds

• Run inference workers in sandboxed containers with seccomp, no-new-privileges, and read-only root filesystems to limit blast radius
• Drop network egress capabilities from inference processes that do not require outbound connectivity
• Validate GGUF files against a strict allow-list of cryptographic hashes before loading
• Isolate model loading in a separate, low-privilege process from inference and application logic

# Configuration example: verify llama.cpp commit and pin GGUF hashes
git -C llama.cpp rev-parse HEAD
# Expect: 26a48ad699d50b6268900062661bd22f3e792579 or newer

# Verify model file integrity before loading
sha256sum model.gguf | grep -f trusted_model_hashes.txt \
  || { echo "Untrusted GGUF file - refusing to load"; exit 1; }