CVE-2026-65102 in DeepStream
Summary
by MITRE • 09/29/2026
NVIDIA DeepStream contains a vulnerability where an attacker could cause an integer overflow by supplying crafted tensor dimensions in a YAML configuration file. A successful exploit of this vulnerability might lead to denial of service, information disclosure, data tampering.
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Analysis
by VulDB Data Team • 09/29/2026
The identified security flaw resides within NVIDIA DeepStream, a comprehensive toolkit designed for building and deploying AI-based video analytics applications on embedded devices and edge platforms. This specific vulnerability is classified as an integer overflow condition that arises during the parsing and processing of YAML configuration files used to define tensor dimensions for neural network inference tasks. In software engineering, particularly in systems handling complex data structures like tensors, proper bounds checking and type safety are critical when converting user-supplied input into internal memory allocations or array indices. The vulnerability occurs because the application fails to adequately validate the magnitude of dimension values provided by the attacker before performing arithmetic operations associated with tensor sizing. When an adversary supplies crafted tensor dimensions that exceed expected limits but remain within a range that triggers integer wrap-around behavior, the resulting calculation produces an incorrect, significantly smaller memory size than required for the actual data structure.
From a technical perspective, this flaw aligns closely with CWE-190, which defines Integer Overflow or Wraparound as a category of weakness where arithmetic operations result in values exceeding the maximum limit of the integer type used by the system. In the context of DeepStream, tensors represent multi-dimensional arrays of data that are fundamental to deep learning inference pipelines. The YAML configuration file serves as a critical control plane input, dictating how these tensors are structured and processed through various stages such as preprocessing, inferencing, and post-processing. By manipulating the dimension parameters within this configuration, an attacker can force the application to allocate insufficient memory buffers or misalign internal pointers. This discrepancy between expected and actual resource allocation creates conditions ripe for buffer overflows, heap corruption, or stack-based exploits depending on how the underlying C++ implementation handles the erroneous size calculations.
The operational impact of exploiting this vulnerability is severe and multifaceted, primarily manifesting as a denial of service against critical video analytics infrastructure. Since DeepStream is often deployed in high-availability environments such as smart cities, industrial automation, or security surveillance systems, crashing the application can lead to significant downtime and loss of real-time monitoring capabilities. Beyond simple availability issues, the vulnerability poses substantial risks for information disclosure and data tampering. If the integer overflow leads to a buffer over-read condition, sensitive memory contents from adjacent processes or internal system states may be leaked to the attacker. Conversely, if the flaw allows for arbitrary write operations due to misaligned pointers resulting from incorrect size calculations, an adversary could modify critical application state, inject malicious logic into the inference pipeline, or escalate privileges on the host device. This transforms a configuration parsing error into a potential remote code execution vector under specific deployment conditions where untrusted YAML inputs are accepted without rigorous sanitization.
This vulnerability is further contextualized by its alignment with MITRE ATT&CK techniques related to input validation failures and resource manipulation. Attackers can leverage this flaw as part of an initial access or persistence strategy, particularly in edge computing scenarios where physical security might be less stringent than in centralized data centers. The ability to crash the service disrupts continuous monitoring capabilities, which is a common objective for adversaries seeking to create blind spots in surveillance networks. Furthermore, if the vulnerability allows for memory corruption that leads to code execution, it facilitates lateral movement within an IoT or edge computing network, allowing attackers to pivot from one compromised device to others sharing similar configurations or trust relationships.
Mitigation strategies must focus on both immediate remediation and long-term architectural improvements. NVIDIA has released patches addressing this integer overflow issue in newer versions of the DeepStream SDK, which include enhanced input validation routines that strictly enforce bounds checking on all tensor dimension parameters before they are processed by memory allocation functions. Organizations relying on affected versions should prioritize updating to the latest stable release as soon as feasible. In addition to software updates, defensive measures such as implementing strict schema validation for YAML configuration files can prevent malformed or excessively large values from reaching the vulnerable code paths. Deploying these configurations in isolated containers with limited resource quotas and privileges reduces the blast radius of a potential exploit. Security teams should also monitor application logs for unusual crashes or memory-related errors that may indicate attempted exploitation, ensuring rapid incident response capabilities are in place to mitigate any successful attacks before they escalate into broader system compromises.