CVE-2026-24255 in Dynamo
Summary
by MITRE • 08/04/2026
NVIDIA Dynamo for Linux contains a vulnerability in the multimodal embedding cache, where an attacker could cause a hash collision by submitting images that share an identical pixel byte sequence but have different dimensions. A successful exploit of this vulnerability might lead to data tampering.
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Analysis
by VulDB Data Team • 08/04/2026
The vulnerability identified in NVIDIA Dynamo for Linux represents a critical security flaw within the multimodal embedding cache mechanism that operates on hash-based data structures. This weakness stems from insufficient hash function collision resistance when processing image inputs with identical pixel byte sequences but varying dimensional properties. The vulnerability specifically affects the system's ability to properly distinguish between different image representations that, despite having the same raw pixel data, differ in resolution or dimensionality parameters.
The technical implementation of this flaw occurs at the hash table level where image embeddings are cached and retrieved based on computed hash values. When images contain identical pixel byte sequences but possess different dimensions, the hash function fails to produce distinct hash codes, resulting in collision scenarios that can be exploited by malicious actors. This particular vulnerability aligns with CWE-254 weakness category which addresses security issues related to hash collisions and inadequate cryptographic hashing implementations. The flaw demonstrates a fundamental failure in input validation and hash computation logic within the multimodal processing pipeline.
From an operational perspective, successful exploitation of this vulnerability could enable attackers to manipulate cached embeddings and potentially corrupt or alter data within the system's multimodal processing framework. The impact extends beyond simple data integrity concerns as it compromises the reliability of the embedding cache mechanism that likely serves as a foundational component for various AI inference tasks. Attackers could leverage this weakness to inject malicious payloads into the caching system, potentially affecting downstream applications that depend on these embedded representations for decision-making processes. This vulnerability particularly threatens environments where NVIDIA Dynamo is deployed for sensitive applications requiring data consistency and integrity.
The mitigation strategies for this vulnerability should focus on implementing robust hash functions with better collision resistance properties specifically designed for multimodal data processing scenarios. System administrators should consider upgrading to patched versions of NVIDIA Dynamo that address the hash collision handling mechanisms. Additionally, implementing input validation checks that prevent identical pixel byte sequences from being processed without proper dimensional verification can help prevent exploitation attempts. Organizations deploying this technology should also consider runtime monitoring and anomaly detection systems that can identify unusual cache behavior patterns indicative of hash collision exploitation attempts.
This vulnerability demonstrates how seemingly innocuous image processing operations can create security risks when underlying data structures lack proper collision handling mechanisms. The flaw represents a classic example of how cryptographic and hashing principles must be carefully applied even in specialized AI frameworks where performance optimizations might inadvertently compromise security controls. Security teams should evaluate their existing deployment environments for similar hash-based caching mechanisms that might be susceptible to analogous collision attacks, particularly in systems processing multimedia content with varying dimensional properties. The vulnerability also highlights the importance of following secure coding practices as outlined in the software security development lifecycle, ensuring that hash functions are selected and implemented based on appropriate security requirements rather than solely performance considerations.
The exploitation of this vulnerability aligns with ATT&CK technique T1566 which involves social engineering through manipulation of data or systems. While not directly a social engineering attack, the mechanism relies on manipulating system data structures to achieve unauthorized access or modification. Organizations should consider implementing comprehensive security assessments that specifically target hash collision vulnerabilities in their AI and machine learning infrastructure components, particularly those involving caching mechanisms and embedded data representations.