CVE Database - Insecure Deserialization

NVIDIA Transformers4Rec for Linux contains a vulnerability where an attacker could cause improper deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.

Mirasvit Full Page Cache Warmer for Magento 2 before version 1.11.12 contains a PHP object injection vulnerability that allows unauthenticated attackers to achieve remote code execution by supplying a crafted serialized PHP object in the CacheWarmer cookie. Attackers can exploit the unrestricted call to PHP's native unserialize() function combined with gadget chains available in Magento and its dependencies to execute arbitrary code on the server.

A flaw has been found in changmingxie tcc-transaction up to 2.1.0. This issue affects the function Fastjson.parseObject of the component Fastjson AutoType REST API. This manipulation causes deserialization. It is possible to initiate the attack remotely. The vendor was contacted early about this disclosure but did not respond in any way.

A critical remote code execution vulnerability exists in all versions of the HuggingFace transformers library prior to version 5.3.0. The vulnerability allows an attacker to craft a malicious `config.json` file containing the `_attn_implementation_internal` field set to an attacker-controlled HuggingFace Hub repository ID. When a victim loads this model using the standard `AutoModelForCausalLM.from_pretrained()` API, the library downloads and executes arbitrary Python code from the attacker's repository with the victim's full OS privileges. This issue arises due to unfiltered deserialization of configuration attributes, insufficient sanitization of internal fields, and unsandboxed execution of downloaded kernels. The vulnerability bypasses the `trust_remote_code` security mechanism, is invisible to the victim, and exploits the standard documented usage pattern, making it particularly severe. Users are advised to upgrade to version 5.3.0 or later to mitigate this issue.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Deserialization of untrusted data in Microsoft Planetary Computer Pro allows an unauthorized attacker to disclose information over a network.

Insecure deserialization in the job results processing component in Amazon Braket SDK before 1.117.0 might allow a remote authenticated user with S3 write access to the job output bucket to achieve arbitrary code execution on any machine that processes job results. We recommend you upgrade to amazon-braket-sdk version 1.117.0 or later.

When adding a key to a remote agent constraint extensions such as [email protected] were not serialized in the request. Destination restrictions were silently stripped when forwarding keys, allowing unrestricted use of the key on the remote host. The client now serializes all constraint extensions. Additionally, the in-memory keyring returned by NewKeyring() now rejects keys with unsupported constraint extensions instead of silently ignoring them.

Concrete CMS 9.5.0 and below is vulnerable to Remote Code Execution due to insecure deserialization occurring in the ExpressEntryList block controller. An rogue administrator with privileges to add blocks to an area can bypass the intended protection mechanism (_fromCIF === true), which normally restricts malicious inputs over form POST requests, by leveraging the REST API functionality. Because the REST API parses requests using json_decode(), the string "true" is evaluated as a strict PHP Boolean(true).  This bypass allows the attacker to inject a malicious serialized payload  into the block's filterFields database column. The payload will subsequently be executed when the block's data is viewed or edited by an administrator leading to complete server takeover (RCE).The Concrete CMS security team gave this vulnerability a CVSS v.4.0 score of 8.9 with a vector of CVSS:4.0/AV:N/AC:H/AT:P/PR:H/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H.  Thanks Nguyễn Văn Thiện https://github.com/Thien225409  for reporting

Deserialization of untrusted data in Apache Fory PyFory. PyFory's ReduceSerializer could bypass documented DeserializationPolicy validation hooks during reduce-state restoration and global-name resolution. An application is vulnerable if it deserializes attacker-controlled data using PyFory Python-native mode with strict mode disabled and relies on DeserializationPolicy to restrict unsafe classes, functions, or module attributes. This issue affects Apache Fory: from before 1.0.0. Mitigation: Users of Apache Fory are recommended to upgrade to version 1.0.0 or later, which enforces DeserializationPolicy validation for the affected ReduceSerializer paths and thus fixes this issue.

NVIDIA BioNemo for Linux contains a vulnerability where a user could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

The Boost plugin for WordPress is vulnerable to PHP Object Injection in versions up to, and including, 2.0.3 via deserialization of untrusted input in the STYXKEY-BOOST_USER_LOCATION cookie. This makes it possible for unauthenticated attackers to inject a PHP Object. No known POP chain is present in the vulnerable software, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present.

NVIDIA TRT-LLM for any platform contains a vulnerability in RPC testing, where an attacker could cause an unsafe deserialization. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.

NVIDIA TRT-LLM for any platform contains a deserialization vulnerability and unsafe serialized handle. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.

NVIDIA TRT-LLM for any platform contains a vulnerability in MPI server, where an attacker could cause an unsafe deserialization. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.

Java Deserialisation Vulnerability in Jaspersoft Reports Library leads to Remote Code Execution (RCE), potentially allowing code execution on the affected system

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

HestiaCP versions 1.9.0 through 1.9.4 contain a deserialization vulnerability in the web terminal component caused by a session format mismatch between PHP and Node.js that allows unauthenticated remote attackers to achieve root-level code execution. Attackers can inject crafted data into HTTP headers that are processed by the PHP session handler but incorrectly deserialized by the Node.js web terminal component as trusted session values, resulting in arbitrary command execution on systems with the web terminal feature enabled.

The Crawler extension passes the X-T3Crawler-Meta response header from crawled URLs directly to PHP's unserialize(). An attacker controlling a crawled endpoint can inject arbitrary serialized PHP objects, leading to Remote Code Execution on the TYPO3 server. Exploitation requires administrative privileges to configure a crawler-enabled page and trigger the crawl via a Scheduler task.

The extension passes an attacker-controlled cookie directly to PHP's unserialize() without safely processing the input. A remote, unauthenticated attacker can supply a crafted serialized payload to trigger PHP Object Injection, leading to Remote Code Execution on the TYPO3 server. Exploitation requires the content element to be configured with "Persistent Mode: Static" in the plugin settings.

AutoGPT is a workflow automation platform for creating, deploying, and managing continuous artificial intelligence agents. In versions 0.6.34 through 0.6.51, the backend deserializes Redis cache bytes using pickle.loads without integrity/authenticity checks. The write path serializes values with pickle.dumps(...) into Redis and the read path blindly invokes pickle.loads(...) on bytes with no HMAC/signature or strict schema validation gating deserialization. If an attacker can poison a shared-cache key in Redis, arbitrary command execution is possible in the backend container context, affecting confidentiality, integrity, and availability. This issue has been fixed in version 0.6.52.

FreePBX is an open source IP PBX. In versions below 16.0.71 and 17.0.6, the backup module does not properly sanitize data during restore operations, potentially leading to compromise if the backup contains carefully crafted hostile data. During backup restore operations, FreePBX extracts selected files from a user-supplied tar archive. If a malicious file exists in the archive, it is read and passed directly to unserialize() without validation, class restrictions, or integrity checks. This issue allows Remote Code Execution during restoration of the backup as the web server user (typically asterisk or www-data). The attack does not require shell access, CLI access, or filesystem write permissions beyond the normal restore workflow. Authentication with a known username that has sufficient access permissions and/or write access to backup files is required. This issue has been fixed in versions 16.0.71 and 17.0.6.

SGLangs multimodal generation runtime is vulnerable to unauthenticated remote code execution when the --enable-custom-logit-processor option is enabled, as Python objects loaded via dill.loads() will be deserialized without validation.

SGLangs multimodal generation runtime scheduler's ROUTER socket binds to 0.0.0.0 by default and contains a sink that calls pickle.loads() on incoming messages, enabling RCE when exposed to the internet.

A security flaw has been discovered in h2oai h2o-3 up to 7402. This affects the function importBinaryModel of the file h2o-core/src/main/java/hex/Model.java of the component JAR Handler. Performing a manipulation results in deserialization. The attack is possible to be carried out remotely. The exploit has been released to the public and may be used for attacks. The vendor was contacted early about this disclosure but did not respond in any way.

A vulnerability was identified in Oinone Pamirs up to 7.2.0. This affects the function JsonUtils.parseMap of the file PamirsParserConfig.java of the component appConfigQuery Interface. Such manipulation leads to deserialization. The attack can be launched remotely. The exploit is publicly available and might be used. The vendor was contacted early about this disclosure but did not respond in any way.

WWW::Mechanize::Cached versions before 2.00 for Perl deserialize cached HTTP responses from a world-writable on-disk cache, enabling local response forgery and code execution. With no explicit cache backend, WWW::Mechanize::Cached constructs a default Cache::FileCache under /tmp/FileCache without overriding the backend's documented directory_umask of 000, so the cache root and its subdirectories are created mode 0777 with no sticky bit. Cache entries are named by sha1_hex of the request and read back through Storable::thaw on the next cache hit. A local attacker with write access to the cache tree can replace a victim's cache entry for a known URL with an arbitrary frozen HTTP::Response blob, causing the victim's next get() of that URL to return attacker controlled response bytes. Because the bytes are passed to Storable::thaw, a victim process that has loaded any class with a side-effectful STORABLE_thaw, DESTROY, or overload hook can be escalated to arbitrary code execution.

DataHub is an open-source metadata platform. Prior to 1.5.0.3, The DataHub frontend (datahub-frontend-react) deserializes attacker-controlled Java objects from the REDIRECT_URL HTTP cookie during the OIDC callback flow, with no integrity protection (no HMAC, no encryption). This is a Deserialization of Untrusted Data vulnerability (CWE-502) affecting the GET /callback/oidc endpoint. Successful exploitation requires a valid user account in the configured OIDC identity provider This vulnerability is fixed in 1.5.0.3.

GitLab has remediated an issue in GitLab EE affecting all versions from 11.9 before 18.9.7, 18.10 before 18.10.6, and 18.11 before 18.11.3 that could have allowed an unauthenticated user to cause denial of service by uploading a specially crafted file due to improper validation.

An authenticated remote code execution vulnerability through undisclosed vectors exists in the BIG-IP and BIG-IQ Configuration utility.  Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

The coreActivity: Activity Logging for WordPress plugin for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 3.0. This is due to the plugin failing to validate or strip PHP serialization syntax from the User-Agent HTTP header before storing it in the logmeta table, and subsequently calling `maybe_unserialize()` on every retrieved `meta_value` in `query_metas()` without verifying the data was originally serialized by the application. This makes it possible for unauthenticated attackers to inject a crafted PHP serialized payload via the User-Agent header during any logged event (such as a failed login attempt), which, when an administrator views the Logs page, is deserialized and passed to `DeviceDetector::setUserAgent()`, triggering a Fatal TypeError that creates a persistent Denial of Service condition blocking administrator access to the Logs page entirely.

Adobe Connect versions 2025.9.15, 2025.8.157 and earlier are affected by a Deserialization of Untrusted Data vulnerability that could result in arbitrary code execution in the context of the current user. An attacker could exploit this vulnerability to execute arbitrary code. Exploitation of this issue requires user interaction in that a victim must visit a maliciously crafted URL or interact with a compromised web page. Scope is changed.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network.

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 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.

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 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.

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.

The CosyVoice project thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its model loading process. When loading model files (.pt) from a user-specified directory (via the --model_dir argument), 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 providing a maliciously crafted model directory containing .pt files with embedded pickle payloads. When a victim loads this directory using CosyVoice's web interface, the malicious payload is executed, leading to remote code execution on the victim's system.

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.

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method 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. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

The snorkel library thru v0.10.0 contains a critical insecure deserialization vulnerability (CWE-502) in the BaseLabeler.load() method of the BaseLabeler class. The method loads serialized labeler models using the unsafe pickle.load() function on user-supplied file paths without any validation or security controls. Python's pickle module is inherently dangerous for deserializing untrusted data, as it can execute arbitrary code during the deserialization process. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the Trainer.load() method of the Trainer class. The method loads model checkpoint 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. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior 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 on the victim's system when the file is loaded.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.