AI & LLM Vulnerabilities

SillyTavern is a locally installed interface for interacting with text generation AI models and related tools. Before version 1.17.0, it had a path traversal vulnerability (a flaw where an attacker can access files outside the intended directory) that allowed authenticated attackers to read and delete arbitrary files like secrets.json and settings.json by manipulating the avatar_url parameter.

SillyTavern, a locally installed interface for interacting with AI text generation models, had a path traversal vulnerability (a flaw that lets attackers write files outside the intended directory) in its /api/chats/import feature prior to version 1.17.0. An authenticated attacker could exploit this by injecting traversal sequences into the character_name field to place malicious files outside the chats directory.

The lodash library has a code injection vulnerability in its `_.template` function (a tool that generates reusable text templates with dynamic values). Attackers can inject malicious code through the `options.imports` parameter, either by passing untrusted input as key names or by exploiting prototype pollution (a technique where attackers modify the default object properties that all objects inherit from). This allows arbitrary code to run when a template is compiled.

ONNX's `save_external_data` method contains a TOCTOU vulnerability (time-of-check-time-of-use, a gap between checking if a file exists and using it) that allows attackers to overwrite arbitrary files by creating symlinks (shortcuts to other files) between those two operations. The code also has a potential path validation bypass on Windows systems that may allow absolute paths to be used.

PraisonAI's `FileTools.download_file()` function has a security flaw called SSRF (server-side request forgery, where a server is tricked into making requests to unintended targets) because it doesn't validate URLs before downloading files. An attacker can make it download from internal services or cloud metadata endpoints, potentially stealing credentials or accessing restricted information.

PraisonAI's SubprocessSandbox has a critical security flaw where it uses `shell=True` (a setting that makes subprocess execute commands through a shell) and only blocks certain command names, but doesn't block `sh` or `bash` executables, allowing attackers to escape the sandbox by running commands like `sh -c '<command>'` even in STRICT mode. This means security protections meant to isolate untrusted AI code can be bypassed, giving attackers access to the network, files, and system information.

PraisonAI's `passthrough()` function accepts a user-controlled `api_base` parameter (the server address to send requests to) and uses it without validation when the primary request method fails. This allows an attacker to make the server send requests to any address it can reach, including internal services like cloud metadata servers that contain sensitive credentials, a vulnerability called SSRF (server-side request forgery, where an attacker tricks a server into requesting internal resources). The flaw affects PraisonAI version 1.5.87 and potentially others.

PraisonAI's `run_python()` function has a shell injection vulnerability (a security flaw where attackers can sneak in operating system commands) because it doesn't properly escape shell metacharacters like `$()` and backticks when building commands. An attacker can inject arbitrary OS commands by embedding `$()` in code passed to the function, leading to full command execution on the system.

ONNX (Open Neural Network Exchange, a standard format for sharing machine learning models) had a vulnerability in versions before 1.21.0 where it didn't properly validate data loaded from model files, allowing an attacker to craft a malicious model that could overwrite internal object properties. An attacker could exploit this by embedding specially crafted metadata (like file paths) into an ONNX model file that would be processed without proper checks.

ONNX (Open Neural Network Exchange, a standard format for sharing machine learning models) versions before 1.21.0 have a path traversal vulnerability via symlink (a shortcut that points to files outside its intended folder), allowing attackers to read arbitrary files outside the model or user-provided directory. This vulnerability has a CVSS score (0-10 severity rating) of 8.7, indicating high severity.

TorchGeo versions 0.4–0.6.0 had a critical vulnerability where the `eval` function (a Python function that executes code from text input) was used in the model weight API, allowing attackers to run arbitrary commands on systems using the library. Any platform exposing TorchGeo's get_weight() or trainers functions publicly was at risk.