Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessObtain local low-priv access
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ExploitSupply crafted GGUF model file
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ExecutionTrigger OOB read in gguf_init_from_file
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ImpactLeak adjacent memory or crash parser
Vulnerability Assessment
| Weakness |
CWE
ASVS 5.0
V15 - Secure Coding and Architecture Bounds checking, read limits
MITRE ATT&CK
1 technique across 1 tactic: Collection The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | Exploitation requires local access to a system running Tiiny-AI PowerInfer and the ability to have a crafted GGUF model file parsed by the gguf_init_from_file function (GGUF Parser) with a manipulated data_offset argument - i.e., the attacker must control or substitute the model file that PowerInfer loads. Per the CVSS vector (AV:L/PR:L/UI:N), the attacker needs local presence and at least low-level privileges but no user interaction. Limiting factors: no remote network vector, the attacker cannot trigger this purely remotely, impact is confined to an out-of-bounds read (information disclosure or crash rather than code execution per the Low C/I/A ratings), and there is no accepted upstream patch yet, so defenders must rely on controlling which GGUF files are loaded. |
| Impact | This is a genuinely low-priority issue despite being a publicly disclosed vulnerability, and the signals are mostly consistent in pointing that way. The CVSS 4.0 vector (AV:L/AC:L/AT:N/PR:L/UI:N) and the low base score of 1.9 indicate a vulnerability that requires local access and at least low-level privileges, with only limited (Low) impact to confidentiality, integrity, and availability - appropriate for a CWE-125 out-of-bounds read triggered by a crafted GGUF model file. It is NOT in CISA KEV, so there is no confirmed active exploitation; the only elevated signal is that public exploit/PoC material exists (the vendor GitHub issue #283). No EPSS score was provided in the input, so exploitation-probability cannot be compared quantitatively. The SSVC/threat sub-vector carries E:P (Proof-of-Concept), which aligns with the PoC-exists signal rather than active exploitation. In short: publicly available exploit code exists but there is no evidence of in-the-wild use, the impact is bounded to an out-of-bounds read (primarily information disclosure or a crash), and exploitation is gated behind local access plus the ability to get the target to parse a malicious GGUF file. No conflicting signals of note, though the absence of EPSS and the unresolved upstream patch (PR awaiting acceptance) are gaps. |
| Exploit ScenarioDerived | A local attacker crafts a malicious GGUF model file with a manipulated data_offset field and convinces a user or process to load it with PowerInfer, causing gguf_init_from_file in ggml.c to read out of bounds. Publicly available exploit material is referenced in the vendor's GitHub issue #283. Given AV:L/PR:L, this is realistically limited to scenarios where the attacker can already supply files on the system or influence which model file is loaded. |
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Alert meAnalysisAIv1 – Oct 13, 2026
An out-of-bounds read in the GGUF model parser of Tiiny-AI PowerInfer (gguf_init_from_file in ggml.c) lets a local attacker who can supply a crafted GGUF file trigger the parser to read past the end of a buffer via a manipulated data_offset value, resulting in memory disclosure or a crash rather than code execution. Only local users with low privileges (CVSS PR:L, AV:L, no user interaction) are able to reach the vulnerable code path, and the attacker must control or substitute the model file that PowerInfer loads; the issue is rated only 1.9 under CVSS 4.0, and our independent assessment aligns with a Low/C:L/I:N/A:L profile on CVSS 3.1. Publicly available exploit code exists (the reported issue includes a proof-of-concept), but there is no confirmed active exploitation (not listed in CISA KEV), and no accepted upstream patch - a pull request addressing the flaw is still awaiting acceptance.
RemediationAI
No vendor-released patch is available: the description states the fixing pull request awaits acceptance, so no fixed commit or release version can be cited, and users should track the upstream repository and issue #283 (https://github.com/Tiiny-AI/PowerInfer/issues/283) for the merge, then rebuild from the corrected commit since the rolling-release model ships no tagged version. Until then, apply compensating controls that target the actual precondition - attacker control of the parsed model file: load only GGUF files from trusted sources and verify their cryptographic hashes before use, and remove write access to model directories (or mount them read-only) so an unprivileged local user cannot drop or swap a malicious model; the trade-off is that legitimate workflows consuming third-party or user-uploaded models must be blocked or gated behind a validation/quarantine step, and this will break public model-hub style ingestion. Run the inference process under a dedicated low-privilege account inside a container or sandbox with a seccomp/read-only filesystem profile and treat parser crashes as security events, which limits exposure if a malformed file is processed but adds operational overhead and can interfere with GPU device access. Additionally, since the failure mode is an out-of-bounds read rather than memory corruption, enabling address-sanitizer or hardened allocator builds during testing can help detect malformed GGUF files early, though such builds are not suitable for production performance. Do not rely on network controls, as exploitation requires local access (AV:L) with low privileges (PR:L) and no user interaction.
Affected VersionsENISA
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Technical ContextAI · show details
PowerInfer is a large-language-model inference engine that reuses the GGML/GGUF stack (ggml.c) to load quantized model weights; GGUF is a binary container format whose header and tensor metadata carry explicit file offsets, including a data_offset field used to compute where tensor data begins. The vulnerable function, gguf_init_from_file, performs this offset arithmetic in ggml.c without adequately validating data_offset against the actual file/buffer size, so a crafted GGUF file causes the reader to dereference or copy bytes beyond the allocated region - the classic CWE-125 out-of-bounds read pattern arising from missing bounds checks on attacker-influenced length/offset fields. The CPE cpe:2.3:a:tiiny-ai:powerinfer:*:*:*:*:*:*:*:* reflects the project's rolling-release model, meaning there are no discrete versioned releases to enumerate; the code state is identified by commit (up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4). Because the impact is confined to reading unintended memory (and potentially crashing the process), the practical consequences are information disclosure and denial of service, not remote or arbitrary code execution, and the attack surface is limited to processes that parse untrusted GGUF files.
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Severity by source
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P
Local crafted-file parsing by a low-priv user drives AV:L/PR:L/AC:L; an out-of-bounds read yields limited memory disclosure (C:L) and possible crash (A:L) with no integrity impact (I:N).
Primary rating from Vendor (VulDB).
CVSS VectorVendor: VulDB
Lifecycle Timeline
3DescriptionCVE.org
A vulnerability was determined in Tiiny-AI PowerInfer up to 8bd56d69906c9d2dba4d3bf6899763401e01a9a4. Impacted is the function gguf_init_from_file of the file ggml.c of the component GGUF Parser. This manipulation of the argument data_offset causes out-of-bounds read. The attack requires local access. The exploit has been publicly disclosed and may be utilized. This product uses a rolling release model to deliver continuous updates. As a result, specific version information for affected or updated releases is not available. The pull request to fix this issue awaits acceptance.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
A buffer overflow occurs when a program writes more data to a memory buffer than it was allocated to hold, causing the excess data to spill into adjacent memory regions.
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
EUVD-2026-98234
GHSA-j6ch-mq7f-f9j6
NVD
CVE.org
EPSS
VulDB
Exploit-DB