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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 retur…
Banks generates meaningful LLM prompts using a simple template language. Prior to 2.5.1, Banks DirectoryPromptRegistry does not reject symbolic links for index.json or discovered and existing .jinja prompt files. In an application where untrusted users can in…
Banks generates meaningful LLM prompts using a simple template language. Prior to 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 cano…
Banks generates meaningful LLM prompts using a simple template language. In versions prior to 2.4.3, banks parses Tool JSON objects from the rendered body of {% completion %} blocks and later resolves their import_path field through importlib.import_module(..…
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_…
Banks generates meaningful LLM prompts using a template language that makes sense. Prior to 2.4.2, banks uses jinja2.Environment() (unsandboxed) to render prompt templates. Applications that pass user-supplied strings as the template argument to Prompt() are …
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