Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A05:2025 - Injection
ASVS 5.0
V1 - Encoding and Sanitization Parameterized queries, prepared statements
MITRE ATT&CK
2 techniques across 2 tactics: Initial Access, Persistence The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Impact | This vulnerability represents exceptionally high real-world risk. CVSS 9.8 with network attack vector (AV:N), low complexity (AC:L), no privileges required (PR:N), and no user interaction (UI:N) indicates trivial exploitation. The attack surface is broad—any web application using llama_index with vector store backends is potentially vulnerable. SQL injection is fully automatable and allows direct data exfiltration or manipulation without requiring additional exploitation steps. The combination of high CVSS, trivial attack requirements, and likely high EPSS score (SQL injection typically scores 0.97+) makes this a critical priority. Without KEV confirmation or POC disclosure data, the actual exploitation timeline is uncertain, but SQL injection is a well-understood attack class with extensive tooling available. Organizations should treat this as requiring emergency patching. The technical impact (CIA triad fully compromised) on confidentiality and integrity is maximum—attackers can read arbitrary data and modify database contents, potentially escalating to application compromise. |
| Exploit ScenarioDerived | An attacker discovers a web application using llama_index v0.12.21 for semantic search over sensitive user data (e.g., customer records, medical information). The application accepts free-text search queries from unauthenticated users. The attacker crafts a malicious search query such as: |
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Alert meAnalysisAIv1 – Mar 14, 2026
Critical SQL injection vulnerability affecting run-llama/llama_index v0.12.21 and potentially other versions, present in multiple vector store integrations. Attackers can execute arbitrary SQL commands without authentication to read and write data, potentially compromising data belonging to other users in web applications leveraging this library. With a CVSS 9.8 severity score, network-accessible attack vector, and no authentication required, this vulnerability poses an immediate and severe risk to production deployments.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 2 packages in pypi, euvd
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
Llama_index is a Python library for building LLM applications with vector store integrations for semantic search and data management. The vulnerability exists in SQL-based vector store adapters (likely PostgreSQL, MySQL, SQLite, or similar database backends) where user-supplied input is improperly sanitized before being concatenated into SQL queries. This is a classic CWE-89 (SQL Injection) vulnerability where dynamic SQL construction fails to use parameterized queries or prepared statements. The library's vector store integration layer accepts inputs (potentially from LLM prompts, user queries, or API parameters) that are passed directly to database drivers without proper escaping or parameterization, allowing attackers to inject malicious SQL syntax. Affected CPE likely includes: cpe:2.3:a:run-llama:llama_index:0.12.21:*:*:*:*:python:*:*
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Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Primary rating from NVD.
CVSS VectorNVD
Lifecycle Timeline
3DescriptionCVE.org
Multiple vector store integrations in run-llama/llama_index version v0.12.21 have SQL injection vulnerabilities. These vulnerabilities allow an attacker to read and write data using SQL, potentially leading to unauthorized access to data of other users depending on the usage of the llama-index library in a web application.
Executive ViewAI
A critical SQL injection vulnerability in llama_index v0.12.21 allows attackers to read and write database data without authorization, potentially exposing customer information and enabling data manipulation. Organizations using this library in web applications face immediate risk of data breach and compliance violations.
24 hours: Inventory all systems running llama_index v0.12.21 and assess exposure in internet-facing applications; immediately isolate affected systems if they handle sensitive data. 7 days: Apply the available vendor patch to all affected systems and validate the remediation in testing environments. 30 days: Conduct a security audit of data access logs to identify any unauthorized access during the vulnerability window and implement enhanced monitoring for SQL injection attempts.
Apply vendor patch. If immediate patching is not possible: implement Web Application Firewall (WAF) rules to block SQL injection patterns; restrict database user permissions to least-privilege access; disable vector store integrations if not actively required; implement database query logging and anomaly detection; segment affected applications from other critical systems.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
SQL injection exploits the way applications construct database queries by mixing user input directly into SQL statements.
Authentication bypass attacks exploit flaws in the verification mechanisms that control access to systems and applications.
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EUVD-2025-16964
GHSA-v3c8-3pr6-gr7p
NVD
CVE.org
EPSS
Exploit-DB