picklescan before 1.0.1 contains an unsafe deserialization vulnerability allowing unauthenticated users to execute arbitrary code by hiding eval calls nested under callable objects via getattr. Attackers can embed malicious code in pickle files that evades detection but executes when the pickle is loaded from untrusted sources.

picklescan before 1.0.4 fails to block at least seven Python standard library modules (including uuid, _osx_support, _aix_support, _pyrepl.pager, and imaplib) exposing eight functions that provide direct arbitrary command execution. Attackers can craft malicious pickle files importing these unblocked modules to achieve remote code execution while bypassing picklescan's safety validation entirely.

picklescan before 1.0.4 contains an incomplete blocklist for the profile module that fails to block the module-level profile.run() function, allowing attackers to achieve arbitrary code execution via exec(). Attackers can craft malicious pickle files calling profile.run(statement) to execute arbitrary Python code while picklescan reports zero security issues.

picklescan before 0.0.33 contains an incomplete deny-list that fails to block pydoc.locate and operator.methodcaller functions, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using these unblocked functions to achieve arbitrary code execution when the pickle is deserialized.

picklescan before 0.0.27 contains a parsing logic error in the _list_globals function when handling STACK_GLOBAL opcodes, failing to track arguments in the correct range and allowing malicious pickle files to bypass detection. Attackers can craft pickle files with arguments at position zero to trigger unexpected exceptions and evade security scanning.

picklescan before 0.0.33 fails to block the ctypes module, allowing attackers to achieve remote code execution by invoking direct syscalls and accessing raw memory. Attackers can craft malicious pickle files using ctypes.WinDLL to load kernel32.dll and execute arbitrary commands, bypassing sandbox protections and gadget chain detection.

picklescan before 0.0.29 fails to detect the profile.Profile.runctx function when analyzing pickle files, allowing attackers to embed undetected malicious code. Remote attackers can craft malicious pickle files using profile.Profile.runctx in the reduce method to achieve remote code execution when the pickle file is loaded.

picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.

picklescan before 0.0.28 fails to detect malicious torch.jit.unsupported_tensor_ops.execWrapper function calls embedded in pickle files. Attackers can craft malicious pickle files that bypass picklescan detection and execute arbitrary code when loaded via pickle.load().

picklescan before 0.0.29 fails to detect malicious pickle files using idlelib.autocomplete.AutoComplete.fetch_completions in reduce methods. Attackers can embed undetected code in pickle files that executes arbitrary commands when loaded by victims.

picklescan before 0.0.29 fails to detect malicious pickle files that exploit idlelib.debugobj.ObjectTreeItem.SetText function in reduce methods. Attackers can craft pickle files with embedded code that bypasses picklescan detection and executes arbitrary commands when pickle.load() is called.

picklescan before 1.0.1 contains an unsafe pickle deserialization vulnerability allowing unauthenticated attackers to create arbitrary zero-byte files via logging.FileHandler class instantiation. Attackers can exploit this by crafting malicious pickle payloads to bypass RCE blocklists and create lock files or other filesystem artifacts, potentially causing denial of service or application disruption.

picklescan before 0.0.28 fails to detect malicious pickle files that invoke torch.utils._config_module.load_config function within reduce methods. Attackers can craft pickle files embedding arbitrary code that evades detection but executes during pickle.load, enabling remote code execution in supply chain attacks.

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.pyshell.ModifiedInterpreter.runcommand in reduce methods. Attackers can embed undetected code in pickle files that executes remote commands when loaded by victims.

picklescan before 0.0.29 fails to detect malicious pickle files that exploit idlelib.autocomplete.AutoComplete.get_entity function in reduce methods. Attackers can embed undetected code in pickle files that executes arbitrary commands when loaded by victims using pickle.load().

picklescan before 0.0.30 fails to detect cProfile.runctx function calls in pickle file reduce methods, allowing attackers to execute arbitrary code. Malicious pickle files bypass picklescan detection and execute remote code when loaded via pickle.load().

picklescan before 0.0.30 (affected versions 0.0.26 and earlier) fails to detect the ensurepip._run_pip built-in function when scanning pickle files, allowing attackers to execute arbitrary code. Malicious pickle files embedding ensurepip._run_pip calls in __reduce__ methods bypass picklescan detection and achieve remote code execution upon pickle.load() invocation.

Deserialization of Untrusted Data vulnerability in Scott My Reading Library my-reading-library allows Object Injection.This issue affects My Reading Library: from n/a through <= 1.0.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted PyTorch model file, leading to arbitrary code execution on the system hosting the Ludwig model server.

An exploitable code execution vulnerability exists in the Levin deserialization functionality of the Epee library, as used in Monero 'Lithium Luna' (v0.12.2.0-master-ffab6700) and other cryptocurrencies. A specially crafted network packet can cause a logic flaw, resulting in code execution. An attacker can send a packet to trigger this vulnerability.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction.

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution.

KollectApps before 4.8.16c is affected by insecure Java deserialization, leading to Remote Code Execution via a ysoserial.payloads.CommonsCollections parameter.

Horovod thru 0.28.1 contains an insecure deserialization vulnerability (CWE-502) in its KVStore HTTP server component. The KVStore server, used for distributed task coordination, lacks authentication and authorization controls, allowing any remote attacker to write arbitrary data via HTTP PUT requests. When a Horovod worker reads data from the KVStore (via HTTP GET), it deserializes the data using cloudpickle.loads() without verifying its source or integrity. An attacker can exploit this by sending a malicious pickle payload to the server before the legitimate data is written, causing the victim worker to deserialize and execute arbitrary code, leading to remote code execution.

Deserialization of Untrusted Data vulnerability in smartdevth Advanced Advertising System advanced-advertising-system allows Object Injection.This issue affects Advanced Advertising System: from n/a through <= 1.3.1.

The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by publishing a malicious model repository on HuggingFace Hub. When a victim loads a model from this repository, arbitrary code is executed on the victim's system in the context of the mamba process.

The imgaug library thru 0.4.0 contains an insecure deserialization vulnerability in its BackgroundAugmenter class within the multicore.py module. The class uses Python's pickle module to deserialize data received via a multiprocessing queue in the _augment_images_worker() method without any safety checks. An attacker who can influence the data placed into this queue (e.g., through social engineering, malicious input scripts, or a compromised shared queue) can provide a malicious pickle payload. When deserialized, this payload can execute arbitrary code in the context of the worker process, leading to remote or local code execution depending on the deployment scenario.

Deserialization of Untrusted Data vulnerability in mywebtonet PHP/MySQL CPU performance statistics mywebtonet-performancestats allows Object Injection.This issue affects PHP/MySQL CPU performance statistics: from n/a through <= 1.2.1.

Deserialization of Untrusted Data vulnerability in sphoid SiteBuilder Dynamic Components sitebuilder-dynamic-components allows Object Injection.This issue affects SiteBuilder Dynamic Components: from n/a through <= 1.0.

Apache OFBiz has unsafe deserialization prior to 17.12.06. An unauthenticated attacker can use this vulnerability to successfully take over Apache OFBiz.

The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script.

SGLang's multimodal generation module is vulnerable to unauthenticated remote code execution through the ZMQ broker, which deserializes untrusted data using pickle.loads() without authentication.

Deserialization of Untrusted Data vulnerability in reputeinfosystems ARPrice arprice allows Object Injection.This issue affects ARPrice: from n/a through <= 4.1.3.

Deserialization of Untrusted Data vulnerability in giveawayboost Giveaway Boost giveaway-boost allows Object Injection.This issue affects Giveaway Boost: from n/a through <= 2.1.4.

SGLang' encoder parallel disaggregation system is vulnerable to unauthenticated remote code execution through the disaggregation module, which deserializes untrusted data using pickle.loads() without authentication.

Deserialization of Untrusted Data vulnerability in AmentoTech Private Limited WPGuppy wpguppy-lite allows Object Injection.This issue affects WPGuppy: from n/a through <= 1.1.0.

The JSONSerializer and CBORSerializer in APScheduler (all versions including 3.10.x and 4.0.0a5) are vulnerable to Remote Code Execution (RCE) via Insecure Deserialization. The unmarshal_object function allows for arbitrary class instantiation and state injection by dynamically importing modules and calling __setstate__ on any class available in the Python environment. An attacker can exploit this by submitting a specially crafted JSON or CBOR payload to an application using these serializers

Qwik is a performance focused javascript framework. qwik <=1.19.0 is vulnerable to RCE due to an unsafe deserialization vulnerability in the server$ RPC mechanism that allows any unauthenticated user to execute arbitrary code on the server with a single HTTP request. Affects any deployment where require() is available at runtime. This vulnerability is fixed in 1.19.1.

Deserialization of Untrusted Data vulnerability in ThemeREX Good Energy goodenergy allows Object Injection.This issue affects Good Energy: from n/a through <= 1.7.7.

Deserialization of Untrusted Data vulnerability in Al Imran Akash Recently recently-viewed-most-viewed-and-sold-products-for-woocommerce allows Object Injection.This issue affects Recently: from n/a through <= 1.1.

The RPC interface in datax-web v1.0.0 and v2.0.0 to v2.1.2 contains no permission checks by default which allows attackers to execute arbitrary commands via crafted Hessian serialized data.

Deserialization of Untrusted Data vulnerability in ThemeREX Pizza House pizzahouse allows Object Injection.This issue affects Pizza House: from n/a through <= 1.4.0.

A deserialization vulnerability in the component \controller\Index.php of Thinkphp v6.1.3 to v8.0.4 allows attackers to execute arbitrary code.

Deserialization of Untrusted Data vulnerability in baptiste.gourdin Talkback talkback-secure-linkback-protocol allows Object Injection.This issue affects Talkback: from n/a through <= 1.0.

manga-image-translator version beta-0.3 and prior in shared API mode contains an unsafe deserialization vulnerability that can lead to unauthenticated remote code execution. The FastAPI endpoints /simple_execute/{method} and /execute/{method} deserialize attacker-controlled request bodies using pickle.loads() without validation. Although a nonce-based authorization check is intended to restrict access, the nonce defaults to an empty string and the check is skipped, allowing remote attackers to execute arbitrary code in the server context by sending a crafted pickle payload.

Hyland OnBase contains an unauthenticated .NET Remoting exposure in the OnBase Workflow Timer Service (Hyland.Core.Workflow.NTService.exe). An attacker who can reach the service can send crafted .NET Remoting requests to default HTTP channel endpoints on TCP/8900 (e.g., TimerServiceAPI.rem and TimerServiceEvents.rem for Workflow) to trigger unsafe object unmarshalling, enabling arbitrary file read/write. By writing attacker-controlled content into web-accessible locations or chaining with other OnBase features, this can lead to remote code execution. The same primitive can be abused by supplying a UNC path to coerce outbound NTLM authentication (SMB coercion) to an attacker-controlled host.

Deserialization of Untrusted Data vulnerability in ThemeREX Kingler kingler allows Object Injection.This issue affects Kingler: from n/a through <= 1.7.

Deserialization of Untrusted Data vulnerability in GMRobbins Disc Golf Manager disc-golf-manager allows Object Injection.This issue affects Disc Golf Manager: from n/a through <= 1.0.0.

Apache Tapestry 3.x allows deserialization of untrusted data, leading to remote code execution. This issue is similar to but distinct from CVE-2020-17531, which applies the the (also unsupported) 4.x version line. NOTE: This vulnerability only affects Apache Tapestry version line 3.x, which is no longer supported by the maintainer. Users are recommended to upgrade to a supported version line of Apache Tapestry.

Deserialization of untrusted data in Nuance PowerScribe allows an unauthorized attacker to execute code over a network.