Security Resilience

CVE-2024-21802

Memory Safety in Ggml Llama.Cpp ≤ 2024-01-09

Public PoCMemory Safety
Published
26 February 2024
Modified
17 June 2026
CVSS Score v3.1 8.8
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
EPSS Score 0.014 71th percentile
Risk Priority 67 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2024-21802 is a high-severity Heap-based Buffer Overflow (CWE-122) vulnerability in Ggml Llama.Cpp. Its CVSS base score is 8.8 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Data Obfuscation (T1001); ranked in the top 29% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as NLP and Transformers; in the Data-Related Vulnerabilities risk domain.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.

EU & UK References

Vulnerability Data

A heap-based buffer overflow vulnerability exists in the GGUF library info->ne functionality of llama.cpp Commit 18c2e17. A specially crafted .gguf file can lead to code execution. An attacker can provide a malicious file to trigger this vulnerability.

CWE(s)

AI Security AnalysisAI

AI Category
NLP and Transformers
Risk Domain
Data-Related Vulnerabilities
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
llama.cpp is a C/C++ library for efficient inference of transformer-based large language models (LLMs) like LLaMA, and the vulnerability is in its GGUF model file parser, a format specifically for LLM model representations.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1001 Data Obfuscation Command And Control
Adversaries may obfuscate command and control traffic to make it more difficult to detect.
T1001.002 Steganography Command And Control
Adversaries may use steganographic techniques to hide command and control traffic to make detection efforts more difficult.
T1003.001 LSASS Memory Credential Access
Adversaries may attempt to access credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS).
T1003.002 Security Account Manager Credential Access
Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored.
T1003.004 LSA Secrets Credential Access
Adversaries with SYSTEM access to a host may attempt to access Local Security Authority (LSA) secrets, which can contain a variety of different credential materials, such as credentials for service accounts.
T1003.006 DCSync Credential Access
Adversaries may attempt to access credentials and other sensitive information by abusing a Windows Domain Controller's application programming interface (API)(Citation: Microsoft DRSR Dec 2017) (Citation: Microsoft GetNCCChanges) (Citati…
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-23496Same product: Ggml Llama.Cpp
CVE-2024-23605Same product: Ggml Llama.Cpp
CVE-2024-21836Same product: Ggml Llama.Cpp
CVE-2024-21825Same product: Ggml Llama.Cpp
CVE-2024-42479Same product: Ggml Llama.Cpp
CVE-2024-45306Shared CWE-122, CWE-787
CVE-2023-49600Shared CWE-122, CWE-787
CVE-2024-27372Shared CWE-122, CWE-787
CVE-2021-21017Shared CWE-122, CWE-787
CVE-2024-39518Shared CWE-122, CWE-787

Affected Assets

ggml
llama.cpp
≤ 2024-01-09

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V1.4.1

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation (including fuzzing and memory-error detectors) can discover heap overflows after they have been coded.

Input validation enforces bounds checking on data written to heap buffers, directly stopping the overflow condition from being introduced.

Requiring documented secure-development standards and tools can mandate bounds-checked coding practices that avoid the weakness.

Security engineering principles require use of memory-safe constructs and bounds-checked allocation routines that avoid introducing heap overflows.

Memory-protection mechanisms limit the ability of a heap overflow to execute attacker-controlled code or corrupt adjacent structures.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

ID.RA-01 partial match
blocks

Vulnerability scanning and recording can discover heap-overflow flaws but does not prevent their introduction in code.

PR.PS-02 partial match
blocks

Timely patching removes known heap-overflow instances after they exist.

PR.PS-06 partial match
blocks

Secure-development practices directly require bounds checking and safe memory handling that prevent heap overflows.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

removes
removes
removes
removes
removes

References