CVE-2024-45201: n/a
An issue was discovered in llama_index before 0.10.38. download/integration.py includes an exec call for import {cls_name}.
AI Analysis
Technical Summary
CVE-2024-45201 is a vulnerability identified in the llama_index Python library, specifically in versions before 0.10.38. The root cause is an unsafe use of the exec function within the download/integration.py module, which dynamically imports classes based on the variable {cls_name}. This pattern corresponds to CWE-94, indicating improper control over code generation or execution. The exec call allows an attacker with low privileges (PR:L) and no user interaction (UI:N) to execute arbitrary Python code remotely (AV:N) with high impact on confidentiality, integrity, and availability (C:H/I:H/A:H). The vulnerability is exploitable over the network without authentication barriers, making it highly dangerous. Although no public exploits have been reported yet, the high CVSS score (8.8) reflects the potential for severe damage. The vulnerability affects software supply chains and applications that rely on llama_index for AI/ML data integration, potentially enabling attackers to execute malicious payloads, steal sensitive data, or disrupt services. The lack of available patches at the time of reporting necessitates immediate attention to usage patterns involving dynamic code execution and input sanitization.
Potential Impact
The impact of CVE-2024-45201 is significant for organizations worldwide that utilize the llama_index library in their AI, machine learning, or data integration workflows. Successful exploitation can lead to remote code execution, allowing attackers to gain full control over affected systems. This can result in data breaches, unauthorized data manipulation, service disruption, and potential lateral movement within networks. The vulnerability's network accessibility and lack of required user interaction increase the risk of automated exploitation campaigns. Organizations relying on vulnerable versions may face operational downtime, reputational damage, and compliance violations if sensitive data is compromised. The threat extends to cloud environments, development pipelines, and production systems where llama_index is integrated, amplifying the scope of potential damage.
Mitigation Recommendations
To mitigate CVE-2024-45201, organizations should: 1) Immediately upgrade llama_index to version 0.10.38 or later once the patch is released to eliminate the unsafe exec usage. 2) In the interim, restrict network access to services using vulnerable versions to trusted users only. 3) Audit and refactor codebases to remove or replace dynamic exec calls with safer alternatives such as explicit imports or factory patterns. 4) Implement strict input validation and sanitization on any user-controllable inputs that influence code execution paths. 5) Employ runtime application self-protection (RASP) or behavior-based detection to monitor for anomalous dynamic code execution. 6) Conduct thorough code reviews and static analysis focusing on dynamic code generation patterns. 7) Monitor security advisories and threat intelligence feeds for emerging exploit attempts targeting this vulnerability. 8) Educate developers on secure coding practices to avoid CWE-94 class vulnerabilities in future development.
Affected Countries
United States, Germany, Japan, South Korea, India, United Kingdom, Canada, France, Australia, Netherlands
CVE-2024-45201: n/a
Description
An issue was discovered in llama_index before 0.10.38. download/integration.py includes an exec call for import {cls_name}.
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
CVE-2024-45201 is a vulnerability identified in the llama_index Python library, specifically in versions before 0.10.38. The root cause is an unsafe use of the exec function within the download/integration.py module, which dynamically imports classes based on the variable {cls_name}. This pattern corresponds to CWE-94, indicating improper control over code generation or execution. The exec call allows an attacker with low privileges (PR:L) and no user interaction (UI:N) to execute arbitrary Python code remotely (AV:N) with high impact on confidentiality, integrity, and availability (C:H/I:H/A:H). The vulnerability is exploitable over the network without authentication barriers, making it highly dangerous. Although no public exploits have been reported yet, the high CVSS score (8.8) reflects the potential for severe damage. The vulnerability affects software supply chains and applications that rely on llama_index for AI/ML data integration, potentially enabling attackers to execute malicious payloads, steal sensitive data, or disrupt services. The lack of available patches at the time of reporting necessitates immediate attention to usage patterns involving dynamic code execution and input sanitization.
Potential Impact
The impact of CVE-2024-45201 is significant for organizations worldwide that utilize the llama_index library in their AI, machine learning, or data integration workflows. Successful exploitation can lead to remote code execution, allowing attackers to gain full control over affected systems. This can result in data breaches, unauthorized data manipulation, service disruption, and potential lateral movement within networks. The vulnerability's network accessibility and lack of required user interaction increase the risk of automated exploitation campaigns. Organizations relying on vulnerable versions may face operational downtime, reputational damage, and compliance violations if sensitive data is compromised. The threat extends to cloud environments, development pipelines, and production systems where llama_index is integrated, amplifying the scope of potential damage.
Mitigation Recommendations
To mitigate CVE-2024-45201, organizations should: 1) Immediately upgrade llama_index to version 0.10.38 or later once the patch is released to eliminate the unsafe exec usage. 2) In the interim, restrict network access to services using vulnerable versions to trusted users only. 3) Audit and refactor codebases to remove or replace dynamic exec calls with safer alternatives such as explicit imports or factory patterns. 4) Implement strict input validation and sanitization on any user-controllable inputs that influence code execution paths. 5) Employ runtime application self-protection (RASP) or behavior-based detection to monitor for anomalous dynamic code execution. 6) Conduct thorough code reviews and static analysis focusing on dynamic code generation patterns. 7) Monitor security advisories and threat intelligence feeds for emerging exploit attempts targeting this vulnerability. 8) Educate developers on secure coding practices to avoid CWE-94 class vulnerabilities in future development.
Technical Details
- Data Version
- 5.1
- Assigner Short Name
- mitre
- Date Reserved
- 2024-08-22T00:00:00.000Z
- Cvss Version
- 3.1
- State
- PUBLISHED
Threat ID: 699f6ceab7ef31ef0b56a332
Added to database: 2/25/2026, 9:43:06 PM
Last enriched: 2/26/2026, 8:10:12 AM
Last updated: 4/12/2026, 3:44:25 PM
Views: 9
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