CVE-2026-4997: PandasAI Path Traversal Vulnerability

CVE-2026-4997 Overview

A path traversal vulnerability has been discovered in Sinaptik AI PandasAI affecting versions up to 3.0.0. The vulnerability exists in the is_sql_query_safe function within the file pandasai/helpers/sql_sanitizer.py. By manipulating input, an attacker can perform path traversal attacks to access files outside of intended directories. The attack can be initiated remotely without authentication, and a public exploit has been disclosed.

Critical Impact

This path traversal vulnerability allows remote attackers to bypass SQL sanitization and potentially access sensitive files on the target system through crafted input manipulation.

Affected Products

• Sinaptik AI PandasAI versions up to 3.0.0
• Systems using the pandasai/helpers/sql_sanitizer.py module
• Applications relying on the is_sql_query_safe function for SQL query validation

Discovery Timeline

• 2026-03-28 - CVE-2026-4997 published to NVD
• 2026-03-30 - Last updated in NVD database

Technical Details for CVE-2026-4997

Vulnerability Analysis

This vulnerability is classified as CWE-22 (Improper Limitation of a Pathname to a Restricted Directory), commonly known as path traversal. The flaw resides in the SQL sanitization helper module of PandasAI, specifically in the is_sql_query_safe function located in pandasai/helpers/sql_sanitizer.py.

The function, designed to validate and sanitize SQL queries, fails to properly restrict pathname manipulation. This allows attackers to craft malicious inputs that traverse outside the intended directory structure. The vulnerability can be exploited remotely without requiring any authentication or user interaction, making it accessible to unauthenticated attackers across the network.

According to the disclosure, the vendor was contacted about this vulnerability but did not respond, and the exploit has been publicly released.

Root Cause

The root cause of this vulnerability lies in insufficient input validation within the is_sql_query_safe function. The SQL sanitizer fails to properly neutralize special path elements such as ../ sequences that could be embedded within SQL queries or related file operations. This oversight allows attackers to escape the intended directory context and access arbitrary files on the file system.

Attack Vector

The attack is network-accessible and requires no authentication or special privileges. An attacker can remotely send crafted requests containing path traversal sequences to the vulnerable function. The manipulation occurs through the SQL query input that is processed by the sanitizer, which fails to detect and block directory traversal patterns.

The vulnerability has been publicly documented with proof-of-concept code available through the GitHub Gist PoC. The public availability of exploit code increases the risk of active exploitation.

Detection Methods for CVE-2026-4997

Indicators of Compromise

• Unexpected file access attempts containing path traversal patterns (../, ..%2f, ..%5c) in application logs
• SQL queries containing directory traversal sequences processed by PandasAI
• Anomalous file read operations outside expected application directories
• Unusual network requests targeting PandasAI endpoints with encoded path characters

Detection Strategies

• Monitor application logs for SQL queries containing path traversal patterns such as ../ or encoded variants
• Implement web application firewall (WAF) rules to detect and block common path traversal sequences
• Deploy file integrity monitoring on sensitive directories to detect unauthorized access attempts
• Use runtime application self-protection (RASP) to identify exploitation attempts in real-time

Monitoring Recommendations

• Enable verbose logging for the PandasAI SQL sanitizer module to capture all query processing activity
• Set up alerts for failed file access attempts or access to files outside application directories
• Monitor for unusual patterns in SQL query inputs that may indicate fuzzing or exploitation attempts
• Review application access logs for requests from known malicious IP addresses or with suspicious user agents

How to Mitigate CVE-2026-4997

Immediate Actions Required

• Upgrade PandasAI to a patched version beyond 3.0.0 when available
• Implement additional input validation layers to sanitize path traversal sequences before they reach the vulnerable function
• Deploy network-level controls such as WAF rules to filter malicious requests
• Consider temporarily disabling or restricting access to functionality that relies on the is_sql_query_safe function

Patch Information

The vendor was contacted about this vulnerability but did not respond. At the time of disclosure, no official patch has been released. Organizations should monitor the VulDB Vulnerability entry and the official PandasAI repository for updates on remediation.

Additional technical details and proof-of-concept information can be found through the VulDB submission.

Workarounds

• Implement a custom wrapper function that validates and sanitizes all inputs before passing them to is_sql_query_safe
• Apply strict input validation to reject any strings containing path traversal sequences (../, ..\\, encoded variants)
• Deploy application-level firewall rules to block requests containing common path traversal patterns
• Limit file system access permissions for the application to minimize impact if exploitation occurs

# Example input validation for path traversal prevention
# Add this validation before calling is_sql_query_safe

# Check for common path traversal patterns
if echo "$user_input" | grep -qE '(\.\./|\.\.\\|%2e%2e%2f|%2e%2e/|\.\.%2f|%2e%2e%5c)'; then
    echo "Potential path traversal detected - blocking request"
    exit 1
fi