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GHSA-32vr-5gcf-3pw2 CVE-2026-39890 PYSEC-2026-462

PraisonAI Vulnerable to Remote Code Execution via YAML Deserialization in Agent Definition Loading

Critical PyPI
Severity
9.8 / 10
Critical · CVSS 3.1
Exploit chance (EPSS)
0.58%
Chance of exploitation in the next 30 days. Higher than 47.72% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-502 Deserialization of Untrusted Data
Fix
4.5.115
Upgrade praisonai to this version or later

Description

Summary

The AgentService.loadAgentFromFile method uses the js-yaml library to parse YAML files without disabling dangerous tags (such as !!js/function and !!js/undefined). This allows an attacker to craft a malicious YAML file that, when parsed, executes arbitrary JavaScript code. An attacker can exploit this vulnerability by uploading a malicious agent definition file via the API endpoint, leading to remote code execution (RCE) on the server.

Details

The vulnerability exists in the YAML deserialization process. The js-yaml library's load function is used without specifying a safe schema (e.g., JSON_SCHEMA or DEFAULT_SAFE_SCHEMA). This enables the parsing of JavaScript functions and other dangerous types. When a malicious YAML file containing a !!js/function tag is parsed, the function is evaluated, leading to arbitrary code execution.

The vulnerable code is located in src/agents/agent.service.ts at line 55.

PoC

An attacker can create a malicious agent YAML file with the following content:

!!js/function >
  function(){ require('child_process').execSync('touch /tmp/pwned') }

Then, upload this file as an agent definition via the API endpoint that uses AgentService.loadAgentFromFile. When the agent is loaded (either during startup or via an API call that triggers loading), the payload will execute the command touch /tmp/pwned, demonstrating arbitrary code execution.

Impact

This vulnerability allows an unauthenticated attacker (if the API endpoint is unprotected) or an authenticated attacker with the ability to upload agent definitions to execute arbitrary code on the server. This can lead to complete compromise of the server, data theft, or further network penetration.

Recommended Fix

Replace the unsafe load method with a safe alternative. Specifically, use the load method with a safe schema, such as JSON_SCHEMA or DEFAULT_SAFE_SCHEMA. For example:

import yaml from 'js-yaml';
import { JSON_SCHEMA } from 'js-yaml';

// In the loadAgentFromFile method
const agent = yaml.load(fileContent, { schema: JSON_SCHEMA });

Alternatively, if the application requires only a subset of YAML features, consider using the safeLoad method from an older version (though note it was deprecated). The key is to avoid loading tags that can execute code.

Additionally, validate and sanitize all user input, especially file uploads. Ensure that agent definition files are only uploaded by trusted users and consider storing them in a secure location with proper access controls.

Affected versions

PyPI

PackageVulnerableFixed in
praisonai
< 4.5.115
4.5.115

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

praisonai PyPI → 4.5.115

pip
pip install --upgrade "praisonai>=4.5.115"
poetry
poetry add "praisonai>=4.5.115"
uv
uv add "praisonai>=4.5.115"

Severity breakdown

CVSS 3.1 9.8

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-502: Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

How to prevent it
  • Architecture and Design, Implementation: If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
  • Implementation: When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
  • Implementation: Explicitly define a final object() to prevent deserialization.
  • Architecture and Design, Implementation: Make fields transient to protect them from deserialization. An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
  • Implementation: Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
  • Architecture and Design, Implementation: Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
  • Operation: Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
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