AI Agent Framework Security Comparison

Built-in container sandboxing with configurable permission levels. Agents run in isolated environments by default. File system access is scoped.

Configurable input/output guardrails. Supports custom safety rules. Rate limiting and cost controls built in.

Tool calls require explicit permission grants. Audit logging for all tool invocations. MCP tool integration with permission scoping.

System prompt isolation from user context. Instruction hierarchy support. No built-in prompt injection detection (relies on model-level defenses).

Secure defaults out of the box. New agents start with restrictive permissions. Explicit opt-in for dangerous capabilities.

Strengths

• Security-first architecture with sandboxing built in from day one
• Granular permission system for tool access
• Audit trail for all agent actions
• Sensible defaults that are restrictive rather than permissive

Weaknesses

• Newer project, smaller community for security auditing
• Prompt injection detection relies on underlying model
• Less ecosystem tooling compared to LangChain

No built-in sandboxing. Python code execution tools run in the same process by default. Container isolation requires external setup.

LangChain has added guardrails modules, but they are opt-in. Many tutorials and examples skip them entirely. The LangSmith platform adds monitoring.

Tools are callable by default once registered. No permission system. The agent decides which tools to use based on the prompt. Custom tool validation is possible but not default.

System and human message types provide some separation. But prompt injection through tool outputs and RAG context is a well-documented attack vector in LangChain applications.

Default configurations prioritize functionality over security. The "getting started" path results in agents with broad tool access and no guardrails.

Strengths

• Largest ecosystem and community
• Extensive documentation and examples
• LangSmith provides good observability
• Active development with improving security features

Weaknesses

• Insecure defaults in standard configurations
• No built-in sandboxing
• Many community examples teach insecure patterns
• RAG pipelines vulnerable to indirect injection by default

No sandboxing. Agents share the same execution environment. Multi-agent communication happens in-process with no isolation boundary.

Role-based task delegation provides some implicit guardrails. But no input validation, output filtering, or safety checks are built in.

Tools are assigned per agent role. But any agent can potentially access any tool through delegation. No permission enforcement beyond role assignment.

Agent-to-agent communication is a unique attack surface. A compromised agent can inject instructions into other agents through task delegation. No built-in protection against this.

Security is not a primary design consideration. The framework optimizes for ease of multi-agent orchestration. Security must be added externally.

Strengths

• Clean API for multi-agent workflows
• Role definitions provide conceptual separation
• Growing community and active development
• Good for prototyping agent teams

Weaknesses

• Agent-to-agent injection is a novel attack surface
• No execution sandboxing
• No built-in guardrails or safety checks
• Shared memory between agents can leak sensitive data

Docker-based code execution is available and documented. But the default configuration runs code locally. Users must explicitly enable Docker sandboxing.

Human-in-the-loop is a core pattern. Configurable approval for agent actions. But automated guardrails and input validation are limited.

Function calling is structured. Code execution can be restricted to specific languages. But tool access control is coarse-grained.

Conversation-based architecture means all context is visible to all agents. System messages provide some separation but are not enforced boundaries.

Better than most frameworks by default, thanks to human-in-the-loop patterns. But Docker sandboxing being opt-in rather than default is a significant gap.

Strengths

• Docker sandboxing available (when enabled)
• Human-in-the-loop as a core pattern
• Microsoft backing with active security research
• Good code execution controls

Weaknesses

• Sandboxing is opt-in, not default
• Multi-agent conversations leak context between agents
• Complex configuration for secure setups
• Default examples often skip security best practices