MervinPraison PraisonAI up to 4.5.127 tools.py importlib.util.spec_from_file_location code injection
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.4 | $0-$5k | 0.00 |
Summary
A vulnerability classified as critical has been found in MervinPraison PraisonAI up to 4.5.127. This affects the function importlib.util.spec_from_file_location of the file tools.py. Performing a manipulation results in code injection.
This vulnerability is cataloged as CVE-2026-40156. The attack must be initiated from a local position. There is no exploit available.
It is recommended to upgrade the affected component.
Details
A vulnerability classified as critical has been found in MervinPraison PraisonAI up to 4.5.127. This affects the function importlib.util.spec_from_file_location of the file tools.py. The manipulation with an unknown input leads to a code injection vulnerability. CWE is classifying the issue as CWE-94. The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment. This is going to have an impact on confidentiality, integrity, and availability. The summary by CVE is:
PraisonAI is a multi-agent teams system. Prior to 4.5.128, PraisonAI automatically loads a file named tools.py from the current working directory to discover and register custom agent tools. This loading process uses importlib.util.spec_from_file_location and immediately executes module-level code via spec.loader.exec_module() without explicit user consent, validation, or sandboxing. The tools.py file is loaded implicitly, even when it is not referenced in configuration files or explicitly requested by the user. As a result, merely placing a file named tools.py in the working directory is sufficient to trigger code execution. This behavior violates the expected security boundary between user-controlled project files (e.g., YAML configurations) and executable code, as untrusted content in the working directory is treated as trusted and executed automatically. If an attacker can place a malicious tools.py file into a directory where a user or automated system (e.g., CI/CD pipeline) runs praisonai, arbitrary code execution occurs immediately upon startup, before any agent logic begins. This vulnerability is fixed in 4.5.128.
It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2026-40156 since 04/09/2026. The exploitability is told to be easy. Attacking locally is a requirement. Technical details of the vulnerability are known, but there is no available exploit. The attack technique deployed by this issue is T1059 according to MITRE ATT&CK.
Upgrading to version 4.5.128 eliminates this vulnerability.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Vendor
Name
Version
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 6.5VulDB Meta Temp Score: 6.4
VulDB Base Score: 5.3
VulDB Temp Score: 5.1
VulDB Vector: 🔒
VulDB Reliability: 🔍
CNA Base Score: 7.8
CNA Vector (GitHub_M): 🔒
CVSSv2
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Code injectionCWE: CWE-94 / CWE-74 / CWE-707
CAPEC: 🔒
ATT&CK: 🔒
Physical: Partially
Local: Yes
Remote: Partially
Availability: 🔒
Status: Not defined
EPSS Score: 🔒
EPSS Percentile: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔒
Upgrade: PraisonAI 4.5.128
Timeline
04/09/2026 CVE reserved04/10/2026 Advisory disclosed
04/10/2026 VulDB entry created
04/10/2026 VulDB entry last update
Sources
Product: github.comAdvisory: GHSA-2g3w-cpc4-chr4
Status: Confirmed
CVE: CVE-2026-40156 (🔒)
GCVE (CVE): GCVE-0-2026-40156
GCVE (VulDB): GCVE-100-356859
Entry
Created: 04/10/2026 20:16Changes: 04/10/2026 20:16 (67)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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