CVE-2026-122189 in ppl.nn
Summary
by MITRE • 11/05/2026
A vulnerability has been found in OpenPPL ppl.nn up to bb84dc9952809b3a5a0036858a3d9f68af1c83d5. The affected element is the function OptGraph::InitQuantization of the file src/ppl/nn/engines/cuda/optimizer/opt_graph.cc of the component CUDA Optimizer. Such manipulation of the argument per_channel/bit_width/tensor_max/tensor_min/scale leads to out-of-bounds read. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. This product does not use versioning. This is why information about affected and unaffected releases are unavailable. The project was informed of the problem early through an issue report but has not responded yet.
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Analysis
by VulDB Data Team • 11/05/2026
The vulnerability identified in OpenPPL ppl.nn, specifically within the CUDA Optimizer component prior to commit bb84dc9952809b3a5a0036858a3d9f68af1c83d5, represents a critical memory safety issue rooted in improper input validation. The flaw resides in the OptGraph::InitQuantization function located in src/ppl/nn/engines/cuda/optimizer/opt_graph.cc. This component is responsible for preparing computational graphs for execution on NVIDIA GPUs by applying quantization parameters to neural network layers, a process essential for optimizing inference performance and reducing memory footprint. The specific technical failure involves the mishandling of several critical arguments: per_channel flags, bit_width specifications, tensor_max values, tensor_min values, and scale factors. When these inputs are manipulated with malicious or malformed data, the function fails to perform adequate bounds checking before accessing internal data structures associated with the quantized tensors.
This lack of validation leads directly to an out-of-bounds read condition, which is categorized under CWE-125: Out-of-bounds Read in standard vulnerability classification systems. In practical terms, this means that when the optimizer processes a graph with improperly configured or adversarially crafted quantization parameters, it attempts to access memory locations outside the allocated buffer boundaries of the tensor metadata or associated arrays. While out-of-bounds reads are often considered less severe than writes because they do not directly allow for arbitrary code execution via memory corruption, they still pose significant risks including information disclosure and potential denial of service through application crashes. The attacker can leverage this flaw to read sensitive data from adjacent memory regions that may contain other tensor values, internal state variables, or even stack-based secrets if the vulnerability is chained with other issues in a larger system architecture.
The operational impact of this vulnerability extends beyond simple local exploitation due to its remote exploitability vector. Since OpenPPL ppl.nn is frequently deployed as part of server-side inference engines serving machine learning models over networks, an attacker can trigger this condition by submitting specially crafted model configurations or preprocessing parameters through the API interface. This aligns with attack patterns described in MITRE ATT&CK framework under techniques related to input validation failures and potentially data exfiltration if the read memory contents are returned to the client via error messages or subsequent processing steps that leak buffer content. The fact that the exploit has been disclosed publicly increases the likelihood of automated scanning tools targeting systems running unpatched versions, thereby elevating the immediate risk profile for any production environment utilizing this specific version range.
Complicating remediation efforts is the project's lack of formal versioning scheme, which obscures clear boundaries between affected and unaffected releases. Without semantic version numbers or tagged release commits that explicitly mark security fixes, administrators cannot easily determine whether their deployment falls within the vulnerable window defined by the commit hash bb84dc9952809b3a5a0036858a3d9f68af1c83d5. This ambiguity necessitates a conservative approach to mitigation, where organizations must assume that any deployment prior to this specific code state is potentially vulnerable unless proven otherwise through rigorous testing or source code auditing. The project maintainers have been notified via issue reports but have not yet responded, leaving the community without an official patch timeline and forcing users to rely on upstream fixes or self-mitigation strategies.
To mitigate this risk in the absence of a direct vendor patch, organizations should implement strict input validation at the API layer before passing quantization parameters into the OpenPPL engine. This includes enforcing reasonable limits on bit_width values, ensuring tensor_max and tensor_min are logically consistent with per_channel settings, and validating scale factors to prevent arithmetic anomalies that might trigger edge cases in the optimizer logic. Additionally, deploying runtime protection mechanisms such as Address Sanitizer (ASan) during development or using memory-safe wrappers can help detect these out-of-bounds accesses early. For production environments, consider isolating inference workloads within containers with restricted capabilities and monitoring for abnormal memory access patterns that could indicate exploitation attempts. Regularly updating to the latest available commit after verifying its stability is also recommended as a primary defense strategy against such low-level memory safety vulnerabilities in deep learning infrastructure components.