Attack Chain

1. An attacker crafts a malicious PraisonAI GitHub template URI containing directory traversal sequences (e.g., github:attacker/repo/template@../../../../outside-target) within the ref portion.
2. A user or automated service loads this crafted URI using PraisonAI's TemplateLoader.load() method.
3. PraisonAI's template resolver (praisonai/templates/resolver.py) captures the owner, repo, template path, and the malicious ref verbatim without segment validation.
4. The _get_cache_path() function in praisonai/templates/cache.py concatenates these unvalidated segments to construct a local cache path, resulting in a path that escapes the intended ~/.praison/cache/templates/ directory.
5. Scenario A (Arbitrary File Write): During the first load, the cache.put() method attempts to write the .cache_meta.json file to the attacker-controlled escaped path.
6. Scenario B (Arbitrary Directory Deletion): If a legitimate cache entry for the same owner/repo/template prefix already exists, a subsequent load with the malicious URI causes cache.put() to first call shutil.rmtree() on the attacker-controlled escaped path, deleting an arbitrary directory.
7. The attacker successfully performs either the creation of .cache_meta.json at an arbitrary location (e.g., corrupting application configuration) or the deletion of an arbitrary directory on the system where PraisonAI is running.
8. This leads to corruption of user configuration, project state, or application data, potentially resulting in denial of service or further compromise.

Impact

Successful exploitation of this path traversal vulnerability can lead to severe consequences for organizations utilizing PraisonAI. An attacker can create arbitrary files, specifically .cache_meta.json, in locations outside the application's intended cache, potentially overwriting critical configuration files or injecting malicious data. More critically, under a specific two-stage scenario, an attacker can trigger the deletion of arbitrary directories via shutil.rmtree(), leading to data destruction, corruption of user or project data, or even a complete denial of service by removing essential system directories. All PraisonAI versions from 2.6.0 up to 4.6.57 are affected.

Recommendation

• Patch PraisonAI immediately when a fix is released for versions >= 2.6.0 and <= 4.6.57 as per GHSA-f44v-7qgw-9gh9.
• Deploy the provided Sigma rules to detect suspicious file write and deletion activities initiated by PraisonAI processes.
• Enable detailed file creation and deletion logging (e.g., Sysmon Event ID 11 for file creation, Event ID 23 for file deletion on Windows; auditd on Linux) for Python processes to activate the detection rules.
• If PraisonAI is used in a critical environment, implement strict path validation within any custom TemplateCache or TemplateLoader implementations to reject absolute paths, . or .. segments, or paths escaping the intended cache root, as suggested in GHSA-f44v-7qgw-9gh9.