CVE-2026-102566 in CTranslate2
Summary
by MITRE • 09/29/2026
CTranslate2 before 4.8.1 contains a heap-based buffer overflow in the binary model loader that fails to validate payload length against allocated buffer size. Attackers can craft malicious model files with oversized payload lengths to write past heap allocation boundaries, causing crashes or arbitrary code execution.
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Analysis
by VulDB Data Team • 09/29/2026
The vulnerability identified within CTranslate2 versions prior to 4.8.1 represents a critical memory safety defect located in the binary model loader component of the library. This software is widely utilized for efficient neural machine translation and inference acceleration, making it a frequent target for integration into production environments where large-scale language models are processed. The core technical flaw stems from an insufficient validation mechanism when parsing serialized model files. Specifically, during the loading process, the application reads metadata regarding payload lengths but fails to rigorously compare these declared sizes against the actual memory buffers allocated on the heap for storage. This lack of boundary checking creates a classic buffer overflow condition where the program proceeds to copy data into fixed-size or dynamically allocated regions without verifying that the source data fits within the destination constraints.
From an operational perspective, this flaw allows remote attackers who can supply malicious model files to trigger arbitrary code execution on affected systems. By crafting a binary model file with an artificially inflated payload length field, an attacker forces the loader to attempt writing more bytes than the allocated heap space permits. This overflow overwrites adjacent memory structures, potentially corrupting critical data such as function pointers or exception handlers stored in nearby heap metadata. In modern operating system environments equipped with common mitigations like Address Space Layout Randomization and Data Execution Prevention, successful exploitation may still result in denial of service through segmentation faults or crashes due to memory corruption. However, under specific conditions where the attacker can control the overflowed data precisely, they can redirect execution flow to inject and run shellcode, thereby gaining full control over the host system running the inference engine.
This vulnerability aligns closely with Common Weakness Enumeration identifier CWE-120, which describes buffer copy without checking size limits, a subset of broader memory corruption issues often categorized under CWE-787 out-of-bounds write. The attack vector leverages the trust placed in input files by automated pipelines and model serving infrastructure. In terms of tactical mapping within the MITRE ATT&CK framework, this flaw facilitates initial access or privilege escalation depending on the context of execution, particularly if the service runs with elevated privileges to handle large datasets efficiently. Attackers may utilize techniques associated with resource injection or binary planting to deliver the malicious payload, exploiting the automated nature of model loading processes that often bypass manual inspection for performance reasons.
Mitigation strategies must prioritize immediate version upgrades to CTranslate2 4.8.1 or later, where this validation logic has been corrected to enforce strict size checks before memory allocation and data copying occur. For environments unable to upgrade immediately due to dependency constraints, defensive measures should include implementing input sanitization at the gateway level if models are ingested via network services, ensuring that file sizes do not exceed expected thresholds for known model architectures. Additionally, enabling heap protection mechanisms such as SafeHeap or using compilers with stack-smashing detection can provide additional layers of defense against exploitation attempts. Security teams should also audit their CI/CD pipelines to ensure that only verified and signed model artifacts are loaded into production inference servers, reducing the attack surface presented by untrusted third-party models.