VAITP

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the Airtable Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the flowise server. This vulnerability is fixed in 3.1.0.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. This vulnerability is fixed in 3.1.0.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, there is a remote code execution vulnerability in AirtableAgent.ts caused by lack of input verification when using Pandas. The user’s input is directly applied to the question parameter within the prompt template and it is reflected to the Python code without any sanitization. This vulnerability is fixed in 3.1.0.

Mako is a template library written in Python. Prior to 1.3.11, TemplateLookup.get_template() is vulnerable to path traversal when a URI starts with // (e.g., //../../../secret.txt). The root cause is an inconsistency between two slash-stripping implementations. Any file readable by the process can be returned as rendered template content when an application passes untrusted input directly to TemplateLookup.get_template(). This vulnerability is fixed in 1.3.11.

Pipecat is an open-source Python framework for building real-time voice and multimodal conversational agents. Versions 0.0.41 through 0.0.93 have a vulnerability in `LivekitFrameSerializer` – an optional, non-default, undocumented frame serializer class (now deprecated) intended for LiveKit integration. The class's `deserialize()` method uses Python's `pickle.loads()` on data received from WebSocket clients without any validation or sanitization. This means that a malicious WebSocket client can send a crafted pickle payload to execute arbitrary code on the Pipecat server. The vulnerable code resides in `src/pipecat/serializers/livekit.py` (around line 73), where untrusted WebSocket message data is passed directly into `pickle.loads()` for deserialization. If a Pipecat server is configured to use LivekitFrameSerializer and is listening on an external interface (e.g. 0.0.0.0), an attacker on the network (or the internet, if the service is exposed) could achieve remote code execution (RCE) on the server by sending a malicious pickle payload. Version 0.0.94 contains a fix. Users of Pipecat should avoid or replace unsafe deserialization and improve network security configuration. The best mitigation is to stop using the vulnerable LivekitFrameSerializer altogether. Those who require LiveKit functionality should upgrade to the latest Pipecat version and switch to the recommended `LiveKitTransport` or another secure method provided by the framework. Additionally, always follow secure coding practices: never trust client-supplied data, and avoid Python pickle (or similar unsafe deserialization) in network-facing components.

PySpector is a static analysis security testing (SAST) Framework engineered for modern Python development workflows. The plugin security validator in PySpector uses AST-based static analysis to prevent dangerous code from being loaded as plugins. Prior to version 0.1.8, the blocklist implemented in `PluginSecurity.validate_plugin_code` is incomplete and can be bypassed using several Python constructs that are not checked. An attacker who can supply a plugin file can achieve arbitrary code execution within the PySpector process when that plugin is installed and executed. Version 0.1.8 fixes the issue.

LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. Prior to version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK, the LangSmith SDK's output redaction controls (hideOutputs in JS, hide_outputs in Python) do not apply to streaming token events. When an LLM run produces streaming output, each chunk is recorded as a new_token event containing the raw token value. These events bypass the redaction pipeline entirely — prepareRunCreateOrUpdateInputs (JS) and _hide_run_outputs (Python) only process the inputs and outputs fields on a run, never the events array. As a result, applications relying on output redaction to prevent sensitive LLM output from being stored in LangSmith will still leak the full streamed content via run events. Version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK fix the issue.

A flaw was found in InstructLab. The `linux_train.py` script hardcodes `trust_remote_code=True` when loading models from HuggingFace. This allows a remote attacker to achieve arbitrary Python code execution by convincing a user to run `ilab train/download/generate` with a specially crafted malicious model from the HuggingFace Hub. This vulnerability can lead to complete system compromise.

pyLoad is a free and open-source download manager written in Python. Versions up to and including 0.5.0b3.dev97 cache `role` and `permission` in the session at login and continues to authorize requests using these cached values, even after an admin changes the user's role/permissions in the database. As a result, an already logged-in user can keep old (revoked) privileges until logout/session expiry, enabling continued privileged actions. This is a core authorization/session-consistency issue and is not resolved by toggling an optional security feature. Commit e95804fb0d06cbb07d2ba380fc494d9ff89b68c1 contains a fix for the issue.

The Home Assistant Command-line interface (hass-cli) is a command-line tool for Home Assistant. Up to 1.0.0 of home-assitant-cli an unrestricted environment was used to handle Jninja2 templates instead of a sandboxed one. The user-supplied input within Jinja2 templates was rendered locally with no restrictions. This gave users access to Python's internals and extended the scope of templating beyond the intended usage. This vulnerability is fixed in 1.0.0.