Banks vulnerabilities

Banks generates meaningful LLM prompts using a simple template language. Prior to 2.5.0, Banks Prompt.chat_messages() attempts to parse every line of rendered template output as ChatMessage JSON. When an application renders untrusted data and passes the returned ChatMessage objects to an LLM provider, attacker-controlled JSON can cross the prompt boundary and become a system, assistant, or tool message because ChatMessage.role accepts arbitrary strings. This can override application instructions, alter the intended prompt structure, or confuse downstream tool and message handling. This issue is fixed in version 2.5.0.

Banks generates meaningful LLM prompts using a simple template language. Prior to 2.5.1, DirectoryPromptRegistry does not reject symbolic links for index.json or .jinja prompt files, so in applications where untrusted users can influence a prompt directory, it can follow a link outside the registry root and disclose, read, or overwrite external files.

In Banks before version 2.4.5, DirectoryPromptRegistry.set() in src/banks/registries/directory.py interpolates attacker-controlled Prompt.name and Prompt.version values into a Path without canonicalization or containment validation. Relative traversal such as ../victim/foo and an absolute Prompt.name can escape or discard the configured registry root, while overwrite=True permits replacement of existing target files. The poisoned name is persisted in index.json and reconstructed by _load(), allowing the out-of-root path to survive later registry loads. An application that forwards request data into these fields can therefore write Prompt.raw bytes to attacker-chosen paths writable by the application process. This issue is fixed in version 2.4.5.

Banks prior to version 2.4.3 contains a remote code execution vulnerability in {% completion %} blocks. An attacker can supply a malicious Tool JSON object with an import_path field that is resolved via importlib.import_module() and getattr() without any allowlist. When the LLM invokes a tool with a matching name, arbitrary Python code such as os.system or subprocess.getoutput can be executed.

Banks generates meaningful LLM prompts using a simple template language. In versions prior to 2.4.4, all four media filters (image, audio, video, document) in banks accept untrusted user input as file paths via Path(value) and pass them directly to open(file_path, "rb") without any path sanitization, canonicalization, or directory restriction. An attacker who controls template variables passed to a banks Prompt can use path traversal (../) to read arbitrary files accessible to the Python process—including .env files, SSH keys, cloud credentials, source code, /etc/passwd, and /etc/shadow—with the content returned base64-encoded in the rendered prompt output, making exfiltration trivial.

Banks before version 2.4.2 uses unsandboxed jinja2.Environment() to render prompt templates, allowing Server-Side Template Injection (SSTI) leading to Remote Code Execution (RCE) on the host.