Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:HSummary
CVE-2024-42479 is a critical-severity Write-what-where Condition (CWE-123) vulnerability in Ggml Llama.Cpp. Its CVSS base score is 10.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked in the top 16% 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 Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-16 (Memory Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
llama.cpp, a C/C++ library providing LLM inference, is affected by CVE-2024-42479, a flaw in the rpc_tensor structure. The unsafe data pointer member permits arbitrary address writing, corresponding to CWE-123 and CWE-787, and carries a CVSS 3.1 score of 10.0 reflecting network-reachable impact with high consequences for confidentiality, integrity, and availability plus scope change.
An unauthenticated remote attacker can supply crafted RPC data to trigger the write primitive, achieving arbitrary memory modification that may lead to code execution or full host compromise in affected deployments.
The issue is resolved in commit b3561, per the associated GitHub security advisory GHSA-wcr5-566p-9cwj, which directs users to apply the patch.
EPSS remains low at a current value of 0.0568 with a peak of 0.0586 and shows no material rise; the vulnerability is relevant to AI/ML environments that expose llama.cpp RPC interfaces for distributed inference.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-39639
Vulnerability Data
llama.cpp provides LLM inference in C/C++. The unsafe `data` pointer member in the `rpc_tensor` structure can cause arbitrary address writing. This vulnerability is fixed in b3561.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- llama.cpp is a C/C++ library specifically for LLM (Large Language Model) inference, which relies on transformer architectures central to NLP tasks.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 4 hardening rules · 4 OS baselines
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation (including fuzzing and bounds checks) finds out-of-bounds write flaws before deployment.
Memory-protection mechanisms block unauthorized writes to arbitrary locations even if a write-what-where primitive exists.
Requiring documented secure-development standards and tools can mandate bounds-checked coding practices that avoid the weakness.
Secure engineering principles require memory-safe coding and bounds checking that eliminate the root cause of write-what-where flaws.
Process isolation confines the blast radius of an arbitrary write so it cannot affect other domains.
Input validation directly stops malformed data from triggering buffer overflows that produce arbitrary write-what-where conditions.
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.
Secure SDLC practices directly prevent arbitrary write conditions via safe coding, bounds checking, and memory-safe constructs.
Vulnerability scanning and recording can discover out-of-bounds write flaws so they can be remediated.
Patching or replacing vulnerable software directly eliminates known instances of this coding weakness.
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.
Security testing in development and acceptance can detect write-what-where conditions before deployment.
Secure development lifecycle practices directly reduce the likelihood of write-what-where flaws such as buffer overflows.
Application security requirements can mandate input validation and bounds checking that mitigate arbitrary write conditions.
Secure architecture and engineering principles discourage unsafe memory handling that leads to write-what-where vulnerabilities.
Secure coding standards explicitly forbid unsafe buffer operations that enable arbitrary memory writes.
Change management processes help ensure security fixes for such weaknesses are properly deployed.
Hardening callouts derived
Configuration rules from DISA STIG baselines that bear on weaknesses of the type cited by this CVE. Each rule is shown with the relationship its mapping actually records, against the CWE it was authored against. Derived via CVE→CWE over `controls_xwalks` (authoritative rows only; rows rated `none` are excluded).
Oracle Linux 8 (1 rule)
- V-248592 OL 8 must clear memory when it is freed to prevent use-after-free attacks. prevents CWE-123
RHEL 8 (1 rule)
- V-230279 RHEL 8 must clear memory when it is freed to prevent use-after-free attacks. prevents CWE-123
Windows 10 (1 rule)
- V-220727 Structured Exception Handling Overwrite Protection (SEHOP) must be enabled. prevents CWE-123
Windows 11 (1 rule)
- V-253284 Structured Exception Handling Overwrite Protection (SEHOP) must be enabled. prevents CWE-123