ENTERPRISE AI ANALYSIS
Securing LLM Agentic Networks with Dynamic Firewalls
This analysis delves into a novel framework, inspired by network security principles, to protect dynamic LLM agentic networks. It addresses communication vulnerabilities by automatically deriving task-specific firewall rules from prior conversations, ensuring privacy, integrity, and utility against malicious entities.
Executive Impact: Mitigating Risks in LLM Agent Interactions
Our framework demonstrates significant improvements in privacy and security, crucial for deploying autonomous LLM agents in sensitive enterprise environments.
0%
Reduction in Private Data Leakage
0%
Elimination of State Manipulation Attacks
High
Utility & Adaptability
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Input Firewall
Data Firewall
Trajectory Firewall
Input Firewall: Preventing Malicious Injections
The Input firewall transforms free-form external language into a fully controlled, deterministic language. This process, derived from previous benign conversations, programmatically verifies adherence to the language, fundamentally preventing prompt injections and jailbreaks. It ensures that only pre-generated, allowed inputs can influence the agent's decision-making.
Input Firewall Process
External Agent Input
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Transform to Key-Value Pairs
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Programmatic Verification
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Drop Invalid Keys/Values
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Replace Free-Form Text with Identifiers
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Sanitized Input to Assistant
274
Unique Keys in Generated Input Language
Data Firewall: Ensuring Contextual Privacy
The Data firewall acts as a barrier between the agent and the user's private environment. It abstracts sensitive personal data into a non-private form, guided by policies derived from both benign and attack conversations. This ensures data minimization and prevents leakage while preserving the utility needed for personalized decision-making.
Data Firewall Process
Assistant Queries User Environment
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Raw Data Returned
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Data Firewall (LLM)
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Apply Abstraction Policies
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Abstracted Data to Assistant
| Attack Type | Without Firewall (Leakage %) | With Data Firewall (Leakage %) |
|---|---|---|
| Medical data | 70% | 0% |
| Previous trips | 42% | 0% |
| Purchase history | 42% | 2% |
| Calendar entries | 25% | 0% |
| Access code | 30% | 0% |
Trajectory Firewall: Optimizing Agent Decisions
The Trajectory firewall inspects and corrects intermediate actions and decisions made by the agent. By learning from previous mistakes in attack scenarios, it guides the agent towards more optimal choices that align with the user's preferences and task goals, preventing sub-optimal outcomes like upselling or preference manipulation.
Trajectory Firewall Process
Assistant Generates Answer
→
Trajectory Firewall (LLM)
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Self-Verification with Policies & History
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Corrected/Approved Answer to External Party
| Metric | Task-confined | Firewalled |
|---|---|---|
| Activities Rating (↑) | 7.61/10 | 8.64/10 |
| Restaurants Rating (↑) | 6.61/10 | 7.59/10 |
| Remaining Budget (↑) | 210.35 | 251.72 |
| Additional Packages (↓) | 1.35 | 1.05 |
Quantify Your AI Investment Return
Understand the potential financial and efficiency gains for your enterprise by implementing secure LLM agentic networks.
AI Agent ROI Estimator
Your Roadmap to Secure AI Integration
We provide a structured approach to deploying firewalled LLM agents, ensuring a smooth and secure transition for your enterprise.
Discovery & Strategy
Initial assessment of your current LLM agent usage, identification of critical data flows, and definition of custom firewall policies based on your enterprise's security and privacy needs. This phase establishes the foundation for your secure agentic network.
Firewall Development & Training
Design and implement custom input, data, and trajectory firewalls. We leverage your historical agent interactions to train the LLM-based firewalls, ensuring they adapt precisely to your operational context and learn from past vulnerabilities.
Integration & Testing
Seamless integration of the firewalls into your existing LLM agent architecture. Rigorous testing with simulated benign and malicious interactions to validate security effectiveness, privacy preservation, and maintain agent utility and adaptability.
Deployment & Monitoring
Launch of the firewalled LLM agentic network. Continuous monitoring and iterative refinement of firewall policies to adapt to evolving threats and operational requirements, ensuring long-term security and optimal performance.
Ready to Secure Your LLM Agentic Future?
Connect with our experts to explore how dynamic firewalls can protect your enterprise's AI interactions, ensuring privacy, integrity, and operational efficiency.
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