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 llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec() function without any sanitization, sandboxing, or security restrictions. An attacker can exploit this by crafting a malicious llm command with arbitrary Python code in the --functions argument and using social engineering to trick a victim into running it. This leads to arbitrary code execution on the victim's system, potentially granting the attacker full cont
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 o
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 cod
Guardrails AI thru 0.6.7 contains a code injection vulnerability (CWE-94) in its Hub package installation mechanism. When installing validator packages via guardrails hub install, the system retrieves a manifest from the Guardrails Hub and dynamically executes a script specified in the post_install field. The script path is constructed from untrusted manifest data and executed without proper validation or sanitization, allowing remote code execution. An attacker who can publish malicious packages to the Hub can inject arbitrary code that will be executed on any system where a victim installs the malicious package.
The superduper project thru v0.10.0 contains a critical remote code execution vulnerability in its query parsing component. The _parse_op_part() function in query.py uses the unsafe eval() function to dynamically evaluate user-supplied query operands without proper sanitization or restriction. Although the function attempts to limit the execution context by providing a restricted global namespace, it does not block access to dangerous built-in functions. A remote attacker can exploit this by submitting a specially crafted query string containing Python code that imports modules (e.g., os) and executes arbitrary system commands, leading to com
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 vulnerabl
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.
PySyft (Syft Datasite/Server) versions 0.9.5 and earlier are vulnerable to remote code execution due to insufficient validation and sandboxing of user-submitted code. The system allows low-privileged users to submit Python functions (via @sy.syft_function()) for remote execution on the server. While a code approval mechanism exists, the submitted code undergoes no security checks for dangerous operations (e.g., file access, command execution). Once approved, the code is executed within the server process using exec() and eval() functions without proper isolation. A remote attacker can leverage this to execute arbitrary Python code on the serv
An issue in Open Source Kubectl MCP Server v1.1.1 allows attackers to execute arbitrary code on a victim system via user interaction with a crafted HTML page.
Media · CVSS 6.8EPSS 0,34%Sin corrección publicada
Wireshark MCP: Arbitrary file write via export_objects when WIRESHARK_MCP_ALLOWED_DIRS is not configured
Wireshark MCP is an MCP Server that turns tshark into a structured analysis interface, then layers in optional Wireshark suite utilities. In 1.1.5 and earlier, wireshark-mcp exposes a wireshark_export_objects MCP tool that accepts an attacker-controlled dest_dir parameter and passes it to tshark's --export-objects flag with no mandatory path restriction. The path sandbox (_allowed_dirs) is None by default and only activates when the environment variable WIRESHARK_MCP_ALLOWED_DIRS is explicitly set. In a default installation, any directory on the filesystem can be used as the export destination.
Microdot: HTTP response splitting in Response.set_cookie()
Microdot is a minimalistic Python web framework. Prior to 2.6.1, the Response.set_cookie() method does not sanitize its string arguments, and in particular will not detect the presence of the \r\n sequence in them. This can be a potential source of header injection attacks. For a header injection attack through this issue to be possible, an attacker must first infiltrate the client (for example through an independent XSS attack), so that it can send malicious information that is destined to be stored in a cookie by the server on behalf of the victim. An attacker that infiltrates one client can only orchestrate a header injection attack for th
Alta · CVSS 7.5EPSS 3,69%Código de explotación públicoCorrección disponible
Arbitrary File Read via Prompt Tag Source Validation Bypass in mlflow/mlflow
A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality comprom
A weakness has been identified in aiwaves-cn agents up to e8c4e3c2d19739d3dff59e577d1c97090cc15f59. Affected by this issue is the function recall_relevant_memories_to_working_memory of the file core/cat/looking_glass/stray_cat.py of the component cheshire_cat_core. This manipulation causes resource consumption. Remote exploitation of the attack is possible. The exploit has been made available to the public and could be used for attacks. This product follows a rolling release approach for continuous delivery, so version details for affected or updated releases are not provided. The project was informed of the problem early through an issue rep
FireFighter: Unauthenticated SSRF in Raid jira_bot endpoint allows IAM credential theft
FireFighter is an incident management application. Prior to 0.0.54, the POST /api/v2/firefighter/raid/jira_bot endpoint (CreateJiraBotView) is reachable without authentication (permission_classes = [permissions.AllowAny]). Its attachments payload is fetched server-side via httpx.get() with no URL validation, then uploaded as an attachment on the Jira ticket that gets created. An unauthenticated caller able to reach the ingress can coerce the pod into fetching arbitrary URLs and exfiltrate the response as a Jira attachment. On EC2/EKS deployments that do not enforce IMDSv2, this allows theft of the temporary AWS credentials attached to the pod
Open edx Enterprise Service: SSRF via SAML metadata URL in sync_provider_data endpoint
The Open edx Enterprise Service app provides enterprise features to the Open edX platform. From 7.0.2 to 7.0.4, the sync_provider_data endpoint in SAMLProviderDataViewSet fetches SAML metadata from a URL stored in SAMLProviderConfig.metadata_source. An authenticated user with the Enterprise Admin role can set this field to an arbitrary URL via the SAMLProviderConfigViewSet PATCH endpoint, then trigger a server-side HTTP request by calling sync_provider_data. The fetch in fetch_metadata_xml() passes the URL directly to requests.get() with no scheme enforcement, IP filtering, or timeout. This vulnerability is fixed in 7.0.5.