Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafe_imports() method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules will not be detected as unsafe, allowing attackers to bypass Fickling's primary static safety checks. This issue has been patched in version 0.1.7.
Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, both ctypes and pydoc modules aren't explicitly blocked. Even other existing pickle scanning tools (like picklescan) do not block pydoc.locate. Chaining these two together can achieve RCE while the scanner still reports the file as LIKELY_SAFE. This issue has been patched in version 0.1.7.
Fickling is a Python pickling decompiler and static analyzer. Fickling versions up to and including 0.1.6 do not treat Python's cProfile module as unsafe. Because of this, a malicious pickle that uses cProfile.run() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS. If a user relies on Fickling's output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system. This affects any workflow or product that uses Fickling as a security gate for pickle deserialization. This issue has been patched in version 0.1.7.
Fickling is a Python pickling decompiler and static analyzer. Fickling versions up to and including 0.1.6 do not treat Python’s runpy module as unsafe. Because of this, a malicious pickle that uses runpy.run_path() or runpy.run_module() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS. If a user relies on Fickling’s output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system. This affects any workflow or product that uses Fickling as a security gate for pickle deserialization. This issue has been patched in version 0.1.7.
Bio-Formats versions up to and including 8.3.0 perform unsafe Java deserialization of attacker-controlled memoization cache files (.bfmemo) during image processing. The loci.formats.Memoizer class automatically loads and deserializes memo files associated with images without validation, integrity checks, or trust enforcement. An attacker who can supply a crafted .bfmemo file alongside an image can trigger deserialization of untrusted data, which may result in denial of service, logic manipulation, or potentially remote code execution in environments where suitable gadget chains are present on the classpath.
Deserialization of Untrusted Data vulnerability in Digital zoom studio DZS Video Gallery allows Object Injection.This issue affects DZS Video Gallery: from n/a through 12.37.
Deserialization of Untrusted Data vulnerability in Digital zoom studio DZS Video Gallery allows Object Injection.This issue affects DZS Video Gallery: from n/a through 12.25.
Deserialization of Untrusted Data vulnerability in Themify Themify Edmin allows Object Injection.This issue affects Themify Edmin: from n/a through 2.0.0.
A high-severity remote code execution vulnerability exists in feast-dev/feast version 0.53.0, specifically in the Kubernetes materializer job located at `feast/sdk/python/feast/infra/compute_engines/kubernetes/main.py`. The vulnerability arises from the use of `yaml.load(..., Loader=yaml.Loader)` to deserialize `/var/feast/feature_store.yaml` and `/var/feast/materialization_config.yaml`. This method allows for the instantiation of arbitrary Python objects, enabling an attacker with the ability to modify these YAML files to execute OS commands on the worker pod. This vulnerability can be exploited before the configuration is validated, potentially leading to cluster takeover, data poisoning, and supply-chain sabotage.
LMDeploy is a toolkit for compressing, deploying, and serving LLMs. Prior to version 0.11.1, an insecure deserialization vulnerability exists in lmdeploy where torch.load() is called without the weights_only=True parameter when loading model checkpoint files. This allows an attacker to execute arbitrary code on the victim's machine when they load a malicious .bin or .pt model file. This issue has been patched in version 0.11.1.
CVE-2025-68665 is a high-severity deserialization vulnerability in the LangChain JS framework affecting versions prior to 0.3.80/@langchain/core 1.1.8 and langchain 1.2.3. The flaw arises from improper handling of objects containing the 'lc' key during JSON serialization, allowing attacker-controlled data to be interpreted as legitimate LangChain objects during deserialization. This can lead to remote code execution or other malicious actions without requiring authentication or user interaction. The vulnerability impacts confidentiality but not integrity or availability directly.
CVE-2025-68664 is a critical deserialization vulnerability in the LangChain framework versions prior to 0.3.81 and 1.2.5. The vulnerability arises because LangChain's dumps() and dumpd() serialization functions do not properly escape dictionaries containing the 'lc' key, which is internally used to mark serialized objects. Malicious actors can craft user-controlled data with this key to trigger unsafe deserialization, leading to potential remote code execution or unauthorized data manipulation. The vulnerability has a CVSS score of 9.3, indicating high severity with network attack vector, no privileges or user interaction required, and a scope change. European organizations using vulnerable LangChain versions in AI or agent-based applications face risks of confidentiality breaches and integrity violations.
CVE-2025-13715 is a high-severity remote code execution vulnerability in Tencent FaceDetection-DSFD's resnet endpoint caused by deserialization of untrusted data. Exploitation requires user interaction, such as visiting a malicious page or opening a malicious file. The flaw allows attackers to execute arbitrary code with root privileges due to improper validation of user-supplied data. Although no known exploits are currently in the wild, the vulnerability poses significant risks to affected systems. The CVSS score of 7.8 reflects high impact on confidentiality, integrity, and availability, with low attack complexity but requiring user interaction. European organizations using Tencent FaceDetection-DSFD should prioritize patching and implement strict input validation and network segmentation to mitigate risks. Countries with significant technology sectors and Tencent product usage, such as Germany, France, and the UK, are likely to be most affected. Immediate attention is warranted to prevent potential exploitation in sensitive environments.
CVE-2025-13709 is a high-severity remote code execution vulnerability in Tencent TFace's restore_checkpoint function caused by deserialization of untrusted data. Exploitation requires user interaction, such as visiting a malicious webpage or opening a crafted file. The flaw allows attackers to execute arbitrary code with root privileges due to improper validation of user-supplied data. Although no known exploits are currently in the wild, the vulnerability poses a significant risk to affected systems. The CVSS score is 7.8, reflecting high impact on confidentiality, integrity, and availability. European organizations using Tencent TFace, especially in sectors relying on AI and facial recognition technologies, should prioritize patching and implement strict input validation and network segmentation. Countries with strong technology sectors and high adoption of Tencent products, such as Germany, France, and the UK, are most likely to be affected. Immediate mitigation and monitoring are recommended to prevent potential exploitation.
Tencent TFace eval Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent TFace. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the eval endpoint. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27187.
Tencent PatrickStar merge_checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent PatrickStar. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the merge_checkpoint endpoint. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27182.
Tencent NeuralNLP-NeuralClassifier _load_checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent NeuralNLP-NeuralClassifier. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the _load_checkpoint function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27184.
Tencent MimicMotion create_pipeline Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent MimicMotion. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the create_pipeline function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27208.
Tencent MedicalNet generate_model Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent MedicalNet. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the generate_model function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27192.
Tencent HunyuanVideo load_vae Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent HunyuanVideo. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the load_vae function.The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27186.
