CVE-2026-4996
SQL Injection in Sinaptik AI PandasAI pandasai-lancedb Extension
Publication date: 2026-03-28
Last updated on: 2026-04-29
Assigner: VulDB
Description
Description
CVSS Scores
EPSS Scores
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Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| sinaptik_ai | pandasai_lancedb | to 0.1.4 (inc) |
| sinaptik_ai | pandasai | to 0.1.4 (inc) |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-74 | The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component. |
| CWE-89 | The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component. Without sufficient removal or quoting of SQL syntax in user-controllable inputs, the generated SQL query can cause those inputs to be interpreted as SQL instead of ordinary user data. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
CVE-2026-4996 is a critical SQL injection vulnerability in the pandasai-lancedb extension of the PandasAI project. It occurs because several functions in the extension construct SQL queries by directly embedding user-supplied input without proper sanitization or parameterization.
Specifically, methods like delete_question_and_answers, delete_docs, update_question_answer, update_docs, get_relevant_question_answers_by_id, and get_relevant_docs_by_id use unsafe Python f-string interpolation to build SQL WHERE clauses. This allows an attacker to inject malicious SQL code.
For example, if an attacker supplies an id value like "x' OR 1=1 --", the resulting SQL condition becomes always true, causing operations like deletion to affect all rows in the database table. This can lead to complete destruction of AI training data.
How can this vulnerability impact me? :
This vulnerability can have severe impacts including complete destruction of AI training data, resulting in denial of service and loss of AI context.
An attacker can remotely exploit this flaw to delete all records in critical tables, causing total data loss.
Additionally, if read operations are similarly vulnerable, there is potential for data exfiltration.
Overall, it severely degrades system availability, integrity, and reliability.
How can this vulnerability be detected on my network or system? Can you suggest some commands?
This vulnerability can be detected by monitoring for unusual or suspicious SQL queries that include unsanitized user input, especially those containing patterns like "' OR 1=1 --" which indicate SQL injection attempts.
One practical approach is to inspect logs or capture network traffic for API requests that include JSON payloads with suspicious 'ids' fields, such as: {"ids": ["x' OR 1=1 --"]}.
Since the vulnerability involves unsafe string interpolation in SQL queries within the pandasai-lancedb extension, you can also audit the source code or runtime behavior for calls to methods like delete_question_and_answers(), delete_docs(), update_question_answer(), update_docs(), get_relevant_question_answers_by_id(), and get_relevant_docs_by_id() that construct SQL queries using unsanitized inputs.
Specific commands depend on your environment, but example commands to detect suspicious payloads or queries might include:
- Using grep to find suspicious patterns in logs: grep -E "ids.*(\' OR 1=1 --)" /path/to/api/logs
- Using network capture tools like tcpdump or Wireshark to filter for JSON payloads containing suspicious 'ids' values.
- If you have access to the database query logs, search for queries with conditions like: id = 'x' OR 1=1 --
What immediate steps should I take to mitigate this vulnerability?
Immediate mitigation steps include:
- Restrict or disable access to the vulnerable API endpoints that accept user-supplied IDs for deletion or update operations until a patch is applied.
- Implement input validation and sanitization to prevent malicious SQL injection payloads from being processed.
- Modify the vulnerable code to use parameterized queries or prepared statements instead of unsafe string interpolation when constructing SQL queries.
- Monitor logs and network traffic for exploitation attempts and block suspicious IP addresses if possible.
- Backup all AI training data and related databases immediately to prevent data loss in case of exploitation.
How does this vulnerability affect compliance with common standards and regulations (like GDPR, HIPAA)?:
The vulnerability allows unauthenticated attackers to perform SQL injection attacks that can lead to complete destruction of AI training data, causing denial of service and loss of data integrity and availability.
Such data loss and potential data exfiltration risks could negatively impact compliance with standards and regulations like GDPR and HIPAA, which require protection of data integrity, availability, and confidentiality.
Specifically, the loss of data availability and integrity due to mass deletion could violate requirements for data protection and system reliability under these regulations.