CVE-2026-4997 identifies a path traversal vulnerability in the Sinaptik AI PandasAI product, version 3.0.0. The vulnerability resides in the is_sql_query_safe function located in the pandasai/helpers/sql_sanitizer.py file. This function is intended to sanitize SQL queries to prevent unsafe operations. However, due to improper input validation or sanitization, an attacker can craft malicious input that manipulates file paths, enabling traversal outside the intended directory scope. This can lead to unauthorized reading of arbitrary files on the host system. The attack vector is remote and requires no authentication or user interaction, making it relatively easy to exploit. The CVSS 4.0 vector string (AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N/E:P) indicates network attack vector, low attack complexity, no privileges or user interaction required, and partial confidentiality impact. The vendor was informed early but has not responded or issued a patch, and a public exploit has been released, increasing the urgency for mitigation. The vulnerability affects only version 3.0.0 of PandasAI, a tool commonly used in AI-driven data analysis and SQL query processing. The flaw could be exploited to access sensitive configuration files, credentials, or other critical data stored on the server hosting PandasAI, potentially leading to further compromise.

The primary impact of this vulnerability is unauthorized disclosure of sensitive information due to path traversal, which can compromise confidentiality. Attackers can read arbitrary files on the system, potentially exposing credentials, configuration files, or proprietary data. This can facilitate further attacks such as privilege escalation, lateral movement, or data exfiltration. Since the vulnerability requires no authentication and can be exploited remotely without user interaction, it poses a significant risk to exposed systems. Organizations relying on PandasAI 3.0.0 in production environments, especially those processing sensitive or regulated data, face increased risk of data breaches. The lack of vendor response and available public exploits further elevate the threat level. However, the vulnerability does not directly impact system integrity or availability, limiting the scope to confidentiality breaches. The overall medium severity reflects this balance. The impact is particularly critical in sectors like finance, healthcare, and government where data confidentiality is paramount.

1. Immediately isolate and audit all systems running PandasAI version 3.0.0 to identify exposure. 2. Implement strict input validation and sanitization on all user-supplied data, especially SQL queries, to prevent path traversal attempts. 3. Use application-layer firewalls or Web Application Firewalls (WAFs) with custom rules to detect and block path traversal payloads targeting the vulnerable function. 4. Restrict file system permissions for the PandasAI process to the minimum necessary, preventing access to sensitive directories and files. 5. Monitor logs for suspicious access patterns or attempts to read unauthorized files. 6. If feasible, deploy PandasAI within isolated containers or sandboxes to limit potential damage. 7. Engage with the vendor or community to track patch releases and apply updates promptly once available. 8. Consider temporary mitigation by disabling or restricting the use of the is_sql_query_safe function or related SQL query processing until a patch is released. 9. Educate developers and administrators about secure coding practices related to path traversal and input validation in AI and data processing tools.