Affected: PyPI: picklescan

picklescan before 0.0.22 only considers standard pickle file extensions in the scope for its vulnerability scan. An attacker could craft a malicious model that uses Pickle and include a malicious pickle file with a non-standard file extension. Because the malicious pickle file inclusion is not considered as part of the scope of picklescan, the file would pass security checks and appear to be safe, when it could instead prove to be problematic.

Affected: PyPI: langchain-community

A vulnerability in the GraphCypherQAChain class of langchain-ai/langchain-community version 0.2.5 allows for SQL injection through prompt injection. This vulnerability can lead to unauthorized data manipulation, data exfiltration, denial of service (DoS) by deleting all data, breaches in multi-tenant security environments, and data integrity issues. Attackers can create, update, or delete nodes and relationships without proper authorization, extract sensitive data, disrupt services, access data across different tenants, and compromise the integrity of the database.

Affected: PyPI: praisonaiagents

## Summary

The approval system in PraisonAI Agents caches tool approval decisions by tool name only, not by invocation arguments. Once a user approves `execute_command` for any command (e.g., `ls -la`), all subsequent `execute_command` calls in that execution context bypass the approval prompt entirely. Combined with `os.environ.copy()` passing all process environment variables to subprocesses, this allows an LLM agent (potentially via prompt injection) to silently exfiltrate API keys and credentials without further user consent.

## Details

The `require_approval` decorator in `src/praisonai-agents/praisonaiagents/approval/__init__.py:176-178` checks approval status by tool name only:

```python @wraps(func) def wrapper(*args, **kwargs): if is_already_approved(tool_name): # line 177 — checks only tool_name return func(*args, **kwargs) # line 178 — bypasses ALL approval ```

The `mark_approved` function in `registry.py:144-147` stores only the tool name string:

```python def mark_approved(self, tool_name: str) -> None: approved = self._approved_context.get(set()) approved.add(tool_name) # stores "execute_command", not args self._approved_context.set(approved) ```

The approval context is never cleared during agent execution — `clear_approved()` exists (`registry.py:152`) but is never called in the agent's tool execution path (`agent/tool_execution.py`).

Meanwhile, the `ConsoleBackend` UI at `backends.py:95-96` misleads the user:…

Affected: PyPI: dagster-ge

Local File Inclusion in dagster._grpc.impl.get_notebook_data in Dagster 1.10.14 allows attackers with access to the gRPC server to read arbitrary files by supplying path traversal sequences in the notebook_path field of ExternalNotebookData requests, bypassing the intended extension-based check.

Affected: PyPI: urllib3

An issue was discovered in urllib3 before 1.26.5. When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.

Affected: PyPI: vllm

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine. This issue is fixed in version 0.30.0.

Affected: PyPI: vllm

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, flash late-interaction scoring at the /score and /rerank endpoints derives each worker's query_key value from the caller-controlled X-Request-Id header. A concurrent request that reuses a victim's identifier can overwrite the cached query embedding so the victim's documents are scored against the attacker's query, and shared use counters can also cause a late-interaction cache-miss error. This issue is fixed in version 0.30.0.

Affected: PyPI: vllm

vLLM is an inference and serving engine for large language models. Prior to 0.28.0, the default mirrored multimodal LRU cache can commit a media hash in the frontend sender cache during multimodal rendering and before engine admission, while the engine receiver cache never receives the payload if that request is rejected. A later request reusing the same media hash causes MultiModalProcessorSenderCache to send no payload and MultiModalReceiverCache to reach an assertion with the message "Expected a cached item," producing a shared-service availability failure. This issue is fixed in version 0.28.0.

Affected: PyPI: docling

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.82.0 until 2.118.1, HTMLBackendOptions(render_page=True) permits file URLs because HTMLDocumentBackend._get_browser_request_block_reason does not enforce the enable_local_fetch setting or confine local requests to the source document directory. Crafted path-backed HTML can embed a readable local text file in a browser-rendered page image when Playwright is installed. Only filesystem Path inputs are affected because stream inputs use an opaque origin, and the default configuration, command-line interface, docling-serve, and non-rendering backends are not affected. This issue is fixed in 2.118.1.

Affected: PyPI: docling

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.0.0 until 2.131.0, the HTML, JATS, OpenDocument spreadsheet, and BoxNote backends, including docling/backend/html_backend.py, docling/backend/jats_backend.py, and docling/backend/boxnote_backend.py, accept the rowspan and colspan attribute values without an upper bound and execute loops or allocate a table grid proportional to the declared span. A very small document can therefore cause sustained CPU use or multi-gigabyte memory allocation, and the document_timeout setting does not interrupt the single backend conversion call. Export through the TableData.grid property can further materialize the oversized grid. This issue is fixed in 2.131.0.