VAITP

AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Prior to 0.6.48, an authenticated user could achieve Remote Code Execution (RCE) on the backend server by embedding a disabled block inside a graph. The BlockInstallationBlock — a development tool capable of writing and importing arbitrary Python code — was marked disabled=True, but graph validation did not enforce this flag. This allowed any authenticated user to bypass the restriction by including the block as a node in a graph, rather than calling the block's execution endpoint directly (which did enforce the flag). This vulnerability is fixed in 0.6.48.

Crawl4AI versions prior to 0.8.0 contain a remote code execution vulnerability in the Docker API deployment. The /crawl endpoint accepts a hooks parameter containing Python code that is executed using exec(). The __import__ builtin was included in the allowed builtins, allowing unauthenticated remote attackers to import arbitrary modules and execute system commands. Successful exploitation allows full server compromise, including arbitrary command execution, file read and write access, sensitive data exfiltration, and lateral movement within internal networks.

DiskCache (python-diskcache) through 5.6.3 uses Python pickle for serialization by default. An attacker with write access to the cache directory can achieve arbitrary code execution when a victim application reads from the cache.

cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. Prior to 46.0.5, the public_key_from_numbers (or EllipticCurvePublicNumbers.public_key()), EllipticCurvePublicNumbers.public_key(), load_der_public_key() and load_pem_public_key() functions do not verify that the point belongs to the expected prime-order subgroup of the curve. This missing validation allows an attacker to provide a public key point P from a small-order subgroup. This can lead to security issues in various situations, such as the most commonly used signature verification (ECDSA) and shared key negotiation (ECDH). When the victim computes the shared secret as S = [victim_private_key]P via ECDH, this leaks information about victim_private_key mod (small_subgroup_order). For curves with cofactor > 1, this reveals the least significant bits of the private key. When these weak public keys are used in ECDSA , it's easy to forge signatures on the small subgroup. Only SECT curves are impacted by this. This vulnerability is fixed in 46.0.5.

LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. The LangSmith SDK's distributed tracing feature is vulnerable to Server-Side Request Forgery via malicious HTTP headers. An attacker can inject arbitrary api_url values through the baggage header, causing the SDK to exfiltrate sensitive trace data to attacker-controlled endpoints. When using distributed tracing, the SDK parses incoming HTTP headers via RunTree.from_headers() in Python or RunTree.fromHeaders() in Typescript. The baggage header can contain replica configurations including api_url and api_key fields. Prior to the fix, these attacker-controlled values were accepted without validation. When a traced operation completes, the SDK's post() and patch() methods send run data to all configured replica URLs, including any injected by an attacker. This vulnerability is fixed in version 0.6.3 of the Python SDK and 0.4.6 of the JavaScript SDK.

The Pydantic-AI MCP Run Python tool configures the Deno sandbox with an overly permissive configuration that allows the underlying Python code to access the localhost interface of the host to perform SSRF attacks. Note - the "mcp-run-python" project is archived and unlikely to receive a fix.

Claude Code is an agentic coding tool. Prior to version 1.0.111, Claude Code contained insufficient URL validation in its trusted domain verification mechanism for WebFetch requests. The application used a startsWith() function to validate trusted domains (e.g., docs.python.org, modelcontextprotocol.io), this could have enabled attackers to register domains like modelcontextprotocol.io.example.com that would pass validation. This could enable automatic requests to attacker-controlled domains without user consent, potentially leading to data exfiltration. This issue has been patched in version 1.0.111.

NiceGUI is a Python-based UI framework. Prior to 3.7.0, NiceGUI's FileUpload.name property exposes client-supplied filename metadata without sanitization, enabling path traversal when developers use the pattern UPLOAD_DIR / file.name. Malicious filenames containing ../ sequences allow attackers to write files outside intended directories, with potential for remote code execution through application file overwrites in vulnerable deployment patterns. This design creates a prevalent security footgun affecting applications following common community patterns. Note: Exploitation requires application code incorporating file.name into filesystem paths without sanitization. Applications using fixed paths, generated filenames, or explicit sanitization are not affected. This vulnerability is fixed in 3.7.0.

NiceGUI is a Python-based UI framework. The ui.markdown() component uses the markdown2 library to convert markdown content to HTML, which is then rendered via innerHTML. By default, markdown2 allows raw HTML to pass through unchanged. This means that if an application renders user-controlled content through ui.markdown(), an attacker can inject malicious HTML containing JavaScript event handlers. Unlike other NiceGUI components that render HTML (ui.html(), ui.chat_message(), ui.interactive_image()), the ui.markdown() component does not provide or require a sanitize parameter, leaving applications vulnerable to XSS attacks. This vulnerability is fixed in 3.7.0.

EPyT-Flow is a Python package designed for the easy generation of hydraulic and water quality scenario data of water distribution networks. Prior to 0.16.1, EPyT-Flow’s REST API parses attacker-controlled JSON request bodies using a custom deserializer (my_load_from_json) that supports a type field. When type is present, the deserializer dynamically imports an attacker-specified module/class and instantiates it with attacker-supplied arguments. This allows invoking dangerous classes such as subprocess.Popen, which can lead to OS command execution during JSON parsing. This also affects the loading of JSON files. This vulnerability is fixed in 0.16.1.