Security Comparison Matrix
Intermediate9 min readUpdated 2026-03-15
Side-by-side security comparison of major AI agent frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel, and OpenAI Assistants, covering default security, common misconfigurations, and framework selection guidance.
Security Comparison Matrix
Choosing an agent framework is a security decision. Each framework makes different trade-offs between developer experience, flexibility, and safety defaults. This page provides a structured comparison of security-relevant features across the five major frameworks, identifies which frameworks are most and least secure by default, and catalogs the most common misconfigurations that create vulnerabilities in each.
Security Feature Comparison
Code Execution Safety
| Framework | Default Code Execution | Sandbox Options | Secure by Default? |
|---|---|---|---|
| LangChain | PythonREPLTool runs on host | E2B, Docker (manual setup) | No |
| CrewAI | Via tools only (no built-in REPL) | Docker (manual setup) | Partial |
| AutoGen | code_execution_config runs locally | Docker supported but not default | No |
| Semantic Kernel | No built-in code execution | N/A | Yes (by absence) |
| OpenAI Assistants | Code Interpreter in managed sandbox | Managed sandbox (not configurable) | Yes |
Tool/Function Calling Safety
| Framework | Parameter Validation | Tool Output Sanitization | Human-in-the-Loop | Call Limits |
|---|---|---|---|---|
| LangChain | Developer responsibility | None | Optional HumanApprovalCallbackHandler | max_iterations (default: 15) |
| CrewAI | Developer responsibility | None | Not built-in | max_iter per agent |
| AutoGen | Developer responsibility | None | human_input_mode configurable | max_consecutive_auto_reply |
| Semantic Kernel | Developer responsibility | None | Auto-invoke vs. manual invoke modes | Developer configurable |
| OpenAI Assistants | Developer responsibility | None | requires_action pattern | Platform-level rate limits |
Memory Security
| Framework | Memory Isolation | Encryption | Access Controls | Audit Logging |
|---|---|---|---|---|
| LangChain | Per-chain (configurable) | Not built-in | Not built-in | Via callbacks |
| CrewAI | Per-crew shared memory | Not built-in | Not built-in | Not built-in |
| AutoGen | Shared conversation | Not built-in | Not built-in | Not built-in |
| Semantic Kernel | Per-kernel configurable | Not built-in | Not built-in | Via middleware |
| OpenAI Assistants | Per-thread (platform-managed) | Platform-managed | API key scoping | Platform audit logs |
Multi-Agent Security
| Framework | Inter-Agent Trust | Agent Isolation | Delegation Controls |
|---|---|---|---|
| LangChain/LangGraph | All agents share context | Graph node boundaries (soft) | Not applicable |
| CrewAI | All agents equally trusted | None -- shared crew context | Delegation on/off per agent |
| AutoGen | All agents equally trusted | None -- shared chat context | Speaker selection only |
| Semantic Kernel | N/A (single-agent) | N/A | N/A |
| OpenAI Assistants | N/A (single-assistant per thread) | Thread isolation | N/A |
Overall Security Ranking
Based on default security posture (how secure the framework is without developer hardening):
| Rank | Framework | Score | Rationale |
|---|---|---|---|
| 1 | OpenAI Assistants | Most secure defaults | Managed sandbox, requires_action pattern, platform-level isolation, no dangerous tools by default |
| 2 | Semantic Kernel | Secure by restraint | No built-in code execution, manual invoke mode available, enterprise-oriented design |
| 3 | CrewAI | Moderate | No built-in REPL, but delegation and shared memory create multi-agent risks |
| 4 | AutoGen | Below average | Local code execution available with minimal configuration, shared conversation context |
| 5 | LangChain | Least secure defaults | PythonREPLTool and ShellTool available, chain composition propagates injection, massive community surface area |
Most Common Misconfigurations
LangChain
| Misconfiguration | Impact | Prevalence |
|---|---|---|
Using PythonREPLTool without sandboxing | Remote code execution | Very High |
No max_iterations limit on agent executor | Resource exhaustion, cost explosion | High |
| Unaudited community tools from LangChain Hub | Supply chain compromise | High |
Direct output passing in SequentialChain without sanitization | Injection propagation | High |
Using SQLDatabaseToolkit with write access | SQL injection leading to data manipulation | Medium |
CrewAI
| Misconfiguration | Impact | Prevalence |
|---|---|---|
| Enabling delegation without access controls | Privilege escalation through delegation | High |
| Shared crew memory without sanitization | Cross-agent memory poisoning | High |
| Using verbose tool descriptions without reviewing them | Schema injection via tool descriptions | Medium |
No max_iter limit per agent | Infinite task loops | Medium |
AutoGen
| Misconfiguration | Impact | Prevalence |
|---|---|---|
Local code_execution_config without Docker | Remote code execution | Very High |
human_input_mode="NEVER" with code execution | Fully autonomous code execution | High |
Unlimited max_consecutive_auto_reply | Conversation loops, cost explosion | Medium |
Using UserProxyAgent with is_termination_msg=None | Never-ending conversations | Medium |
Semantic Kernel
| Misconfiguration | Impact | Prevalence |
|---|---|---|
| Auto-invoke mode for all plugins | No human oversight for sensitive operations | High |
| Registering database connectors without query sanitization | SQL injection | Medium |
| Trusting all plugins equally regardless of source | Supply chain compromise | Medium |
OpenAI Assistants
| Misconfiguration | Impact | Prevalence |
|---|---|---|
| Blindly submitting tool outputs without validation | Standard function calling attacks | Very High |
| Allowing arbitrary file uploads to vector stores | File search poisoning | High |
| Not scoping API keys per assistant/thread | Cross-assistant data access | Medium |
| Ignoring Code Interpreter output in security monitoring | Data processing abuse | Medium |
Framework Selection Decision Guide
Recommendation: OpenAI Assistants or Semantic Kernel
When security is the primary concern:
- OpenAI Assistants provides managed sandboxing, platform-level isolation, and the
requires_actionpattern - Semantic Kernel's conservative defaults and enterprise focus minimize the attack surface
- Avoid LangChain and AutoGen unless you have dedicated security resources to harden them
Recommendation: CrewAI with hardening, or custom LangGraph
When multi-agent collaboration is required:
- CrewAI provides the most structured multi-agent model (roles, goals, tasks) but requires delegation controls
- LangGraph allows custom agent graphs with explicit trust boundaries at node edges
- AutoGen's group chat model is the hardest to secure due to implicit trust between agents
- Implement inter-agent message sanitization regardless of framework
Recommendation: OpenAI Assistants for managed, LangChain for self-hosted
When speed matters but security cannot be ignored:
- OpenAI Assistants: fastest to deploy with reasonable defaults; platform handles sandboxing
- LangChain: fastest to prototype with custom tools; immediately remove
PythonREPLToolandShellTool - Set
max_iterationsand implement basic output sanitization from day one
Recommendation: Semantic Kernel or hardened LangChain
When enterprise requirements (compliance, audit, access control) apply:
- Semantic Kernel: designed for enterprise integration, .NET/Java support, manual invoke mode
- LangChain: largest ecosystem, most flexible, but requires significant hardening
- Both require custom implementations for memory encryption, access control, and audit logging
Hardening Checklist (All Frameworks)
Regardless of framework choice, apply these security controls:
- Remove or sandbox all code execution tools
- Set maximum iteration/call limits per conversation
- Implement human-in-the-loop for sensitive operations
- Sanitize all tool outputs before feeding back to the model
- Audit all registered tools, especially community-contributed ones
- Implement parameter validation for all function calls
- Set up monitoring and alerting for anomalous tool call patterns
- Encrypt sensitive memory contents
- Enforce per-user memory isolation in multi-tenant deployments
- Pin framework versions and monitor for security advisories
Related Topics
- LangChain Security Deep Dive -- Detailed LangChain analysis
- CrewAI & AutoGen Security -- Multi-agent framework analysis
- OpenAI Assistants API Security -- Managed platform analysis
- Agent Framework Security -- Overview of framework vulnerabilities
Knowledge Check
A development team is building a multi-agent system for processing customer support tickets. The agents need code execution for data analysis, and the system will be multi-tenant. Which framework requires the LEAST additional security work for this use case?
References
- LangChain Security Documentation (2025)
- Microsoft AutoGen Documentation (2024)
- Microsoft Semantic Kernel Documentation (2025)
- OpenAI Assistants API Documentation (2025)
- CrewAI Documentation (2025)
- OWASP Top 10 for LLM Applications v2.0