Sinaptik AI PandasAI up to 0.1.4 pandasai-lancedb Extension lancedb.py sql injection
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.6 | $0-$5k | 0.00 |
Summary
A vulnerability was found in Sinaptik AI PandasAI up to 0.1.4. It has been classified as critical. This affects the function delete_question_and_answers/delete_docs/update_question_answer/update_docs/get_relevant_question_answers_by_id/get_relevant_docs_by_id of the file extensions/ee/vectorstores/lancedb/pandasai_lancedb/lancedb.py of the component pandasai-lancedb Extension. Performing a manipulation results in sql injection.
This vulnerability was named CVE-2026-4996. The attack may be initiated remotely. In addition, an exploit is available.
The vendor was contacted early about this disclosure but did not respond in any way.
Details
A vulnerability, which was classified as critical, was found in Sinaptik AI PandasAI up to 0.1.4. This affects the function delete_question_and_answers/delete_docs/update_question_answer/update_docs/get_relevant_question_answers_by_id/get_relevant_docs_by_id of the file extensions/ee/vectorstores/lancedb/pandasai_lancedb/lancedb.py of the component pandasai-lancedb Extension. The manipulation with an unknown input leads to a sql injection vulnerability. CWE is classifying the issue as 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. This is going to have an impact on confidentiality, integrity, and availability.
It is possible to read the advisory at gist.github.com. This vulnerability is uniquely identified as CVE-2026-4996. The exploitability is told to be easy. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. Technical details and a public exploit are known. The attack technique deployed by this issue is T1505 according to MITRE ATT&CK.
The exploit is shared for download at gist.github.com. It is declared as proof-of-concept. The vendor was contacted early about this disclosure but did not respond in any way.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2026-16921). Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Type
Vendor
Name
Version
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 7.3VulDB Meta Temp Score: 6.6
VulDB Base Score: 7.3
VulDB Temp Score: 6.6
VulDB Vector: 🔒
VulDB Reliability: 🔍
CVSSv2
| AV | AC | Au | C | I | A |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Sql injectionCWE: CWE-89 / CWE-74 / CWE-707
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Yes
Availability: 🔒
Access: Public
Status: Proof-of-Concept
Download: 🔒
EPSS Score: 🔒
EPSS Percentile: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔒
Timeline
03/27/2026 Advisory disclosed03/27/2026 VulDB entry created
03/28/2026 VulDB entry last update
Sources
Advisory: gist.github.comStatus: Not defined
CVE: CVE-2026-4996 (🔒)
GCVE (CVE): GCVE-0-2026-4996
GCVE (VulDB): GCVE-100-353883
EUVD: 🔒
scip Labs: https://www.scip.ch/en/?labs.20161013
Entry
Created: 03/27/2026 14:53Updated: 03/28/2026 19:06
Changes: 03/27/2026 14:53 (58), 03/28/2026 19:06 (1)
Complete: 🔍
Submitter: Eric-b
Cache ID: 216::103
Submit
Accepted
- Submit #778268: Sinaptik AI PandasAI <= 0.1.4 SQL Injection (CWE-89) (by Eric-b)
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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