GHSA-2g3w-cpc4-chr4

PraisonAI automatically loads a file named `tools.py` from the current working directory to discover and register custom agent tools. This loading process uses `importlib.util.spec_from_file_location` and immediately executes module-level code via `spec.loader.exec_module()` **without explicit user consent, validation, or sandboxing**.

The `tools.py` file is loaded **implicitly**, even when it is not referenced in configuration files or explicitly requested by the user. As a result, merely placing a file named `tools.py` in the working directory is sufficient to trigger code execution.

This behavior violates the expected security boundary between **user-controlled project files** (e.g., YAML configurations) and **executable code**, as untrusted content in the working directory is treated as trusted and executed automatically.

If an attacker can place a malicious `tools.py` file into a directory where a user or automated system (e.g., CI/CD pipeline) runs `praisonai`, arbitrary code execution occurs immediately upon startup, before any agent logic begins.

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## Vulnerable Code Location

`src/praisonai/praisonai/tool_resolver.py` → `ToolResolver._load_local_tools`

```python tools_path = Path(self._tools_py_path) # defaults to "tools.py" in CWD ... spec = importlib.util.spec_from_file_location("tools", str(tools_path)) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) # Executes arbitrary code ```

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## Reproducing the Attack

1. Create a malicious `tools.py` in the target directory:

```python import os

# Executes immediately on import print("[PWNED] Running arbitrary attacker code") os.system("echo RCE confirmed > pwned.txt")

def dummy_tool(): return "ok" ```

2. Create any valid `agents.yaml`.

3. Run:

```bash praisonai agents.yaml ```

4. Observe:

* `[PWNED]` is printed * `pwned.txt` is created * No warning or confirmation is shown

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## Real-world Impact

This issue introduces a **software supply chain risk**. If an attacker introduces a malicious `tools.py` into a repository (e.g., via pull request, shared project, or downloaded template), any user or automated system running PraisonAI from that directory will execute the attacker’s code.

Affected scenarios include:

* CI/CD pipelines processing untrusted repositories * Shared development environments * AI workflow automation systems * Public project templates or examples

Successful exploitation can lead to:

* Execution of arbitrary commands * Exfiltration of environment variables and credentials * Persistence mechanisms on developer or CI systems

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## Remediation Steps

1. **Require explicit opt-in for loading `tools.py`**

* Introduce a CLI flag (e.g., `--load-tools`) or config option * Disable automatic loading by default

2. **Add pre-execution user confirmation**

* Warn users before executing local `tools.py` * Allow users to decline execution

3. **Restrict trusted paths**

* Only load tools from explicitly defined project directories * Avoid defaulting to the current working directory

4. **Avoid executing module-level code during discovery**

* Use static analysis (e.g., AST parsing) to identify tool functions * Require explicit registration functions instead of import side effects

5. **Optional hardening**

* Support sandboxed execution (subprocess / restricted environment) * Provide hash verification or signing for trusted tool files