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llamaIndex - 💡(How to fix) Fix OWASP Agentic AI Security Assessment -- LlamaIndex [2 comments, 2 participants]

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run-llama/llama_index#20961•Fetched 2026-04-08 00:43:13
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OWASP Agentic AI Top 10 -- Security Assessment

Hi team,

We conducted an OWASP Agentic AI Top 10 (2025) assessment of 27 popular AI agent frameworks as part of ongoing agentic security research. This assessment was performed via static analysis of public source code only -- no systems were accessed or tested remotely.

Assessment Results -- LlamaIndex

CheckOWASP IDSeverityDetail
Unsafe ExecutionAA-03CRITICALsafe_eval() wrapping eval() -- bypassable safety wrapper
Injection PatternAA-02CRITICALexec() in code execution utilities
Excessive PermissionsAA-04MEDIUMHigh-risk permissions: execute
Inadequate SandboxingAA-09HIGHNo process isolation for code execution

Risk Score: 73/100 (FAIL)

Published CVEs Referenced

This is not a new disclosure. These are previously published:

CVEDetail
CVE-2023-39662Code injection vulnerability
CVE-2024-3271Code execution via unsafe eval

Why This Matters

LlamaIndex is widely used for RAG (Retrieval-Augmented Generation) pipelines and agentic applications. The safe_eval() wrapper around eval() and direct exec() usage in code execution utilities create patterns that have resulted in 2 published CVEs. For users building production RAG agents that process untrusted data, these patterns warrant review against the OWASP Agentic AI Top 10.

Agent Security Gates

As part of this research, we have built an open agent security assessment at agentsign.dev where developers and security teams can:

  • Scan any AI agent against the OWASP Agentic AI Top 10 (free, no account required)
  • Get an identity and trust score for agents before deploying to production
  • Gate agent execution via API -- block agents that fail security checks

Out of 27 agents assessed, 17 passed and 10 failed. Full results available on the platform.

Context

We are not claiming to have discovered these vulnerabilities -- all CVEs referenced above were reported by their original researchers. This assessment maps existing known issues to the OWASP Agentic AI Top 10 framework.

Happy to discuss any of these findings.

Raza Sharif Founder, CyberSecAI Ltd agentsign.dev

extent analysis

Fix Plan

To address the security vulnerabilities identified in the LlamaIndex framework, we will focus on the following fixes:

  • Replace safe_eval() with a safer evaluation method
  • Remove exec() usage in code execution utilities
  • Implement process isolation for code execution
  • Review and reduce high-risk permissions

Code Changes

Here are some example code changes to achieve the above fixes:

Replace safe_eval() with ast.literal_eval()

import ast

# Before
result = safe_eval(user_input)

# After
try:
    result = ast.literal_eval(user_input)
except ValueError:
    # Handle invalid input
    pass

Remove exec() usage

# Before
exec(user_code)

# After
# Use a safer execution method, such as a sandboxed environment or a restricted execution context

Implement process isolation

import multiprocessing

# Before
execute_code(user_code)

# After
def execute_code(code):
    # Create a new process for code execution
    p = multiprocessing.Process(target=execute_code_in_process, args=(code,))
    p.start()
    p.join()

def execute_code_in_process(code):
    # Execute code in a separate process
    # ...

Configuration Changes

Review and update the configuration to reduce high-risk permissions, such as removing the execute permission.

Verification

To verify that the fixes worked, re-run the OWASP Agentic AI Top 10 assessment and ensure that the vulnerabilities are no longer present. Additionally, test the updated code with sample inputs to ensure that it behaves as expected.

Extra Tips

  • Regularly review and update the code to ensure that it remains secure and compliant with the OWASP Agentic AI Top 10 framework.
  • Consider using a security assessment tool, such as the one provided at agentsign.dev, to identify and address potential security vulnerabilities.

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#api#ssr#installation#tensor shape#agent execution#task chaining#parallel task#integration issue

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