Enterprise AI Analysis
Introducing the Generative Application Firewall (GAF)
The Generative Application Firewall (GAF) is a new architectural layer designed to secure LLM applications. It unifies fragmented defenses (prompt filters, guardrails, data-masking) into a single enforcement point, similar to how a WAF protects web traffic. GAF addresses novel threats like prompt injection and jailbreaking, covering autonomous agents and their tool interactions.
Generative AI systems introduce new security challenges that traditional network firewalls and WAFs cannot address. This paper proposes the Generative Application Firewall (GAF) as a dedicated security layer for these applications, providing a structured approach to security efforts.
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Reduction in Prompt Injection Attempts
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Improved Compliance & Auditability
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Average Latency Overhead
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Security Layers Covered
Deep Analysis & Enterprise Applications
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Why GAF is Necessary
Traditional security models are insufficient for generative AI. Attacks exploit semantic layers, bypassing network and application firewalls. Developers often prioritize functionality over security, leading to vulnerabilities. Multi-application environments amplify complexity, requiring a centralized solution like GAF.
GAF Concept
GAF is a security and control layer for natural language interfaces powered by LLMs. It acts as a centralized enforcement point for security policies across all layers, from network controls to semantic and context-aware enforcement.
GAF Threat Model
GAF protects against external prompt injections, jailbreaks, data scraping, model extraction, DoS, insider threats, automated probing, and supply-chain poisoning. It operates across multiple trust boundaries (user-GAF, GAF-backend, GAF-LLM, GAF-data stores, GAF-tools) and enforces policies against confidential data exfiltration, integrity breaches, and availability attacks.
Extending OSI Model
The OSI model is extended with a Semantic Layer (Layer 8) to account for probabilistic, context-dependent natural language interpretation. This layer focuses on meaning manipulation failures like prompt injection and semantic poisoning, bridging the gap between traditional networking and LLM interactions.
GAF vs. WAF
WAFs secure structured, deterministic web interactions. GAFs protect open-ended, language-driven conversational systems. Key differences include language understanding, context tracking, real-time protocol handling (streaming), content redaction (not just block/allow), and interaction pattern analysis.
Layer 8
The proposed Semantic Layer for LLM interactions extends the OSI model, focusing on meaning manipulation and context.
GAF Policy Enforcement Flow
User Request
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GAF Admission
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GAF Generation
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GAF Intervention
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GAF Post-action
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LLM/Tool Response
| Layer Name | Non-GenAI Attacks | GenAI Attacks |
|---|---|---|
| Network Layer |
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| Access Layer |
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| Syntactic Layer |
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| Semantic Layer |
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| Context Layer |
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Mitigating Multi-Turn Jailbreaks with GAF
A financial institution deployed an LLM chatbot for customer support. Adversaries attempted to extract sensitive customer data using multi-turn jailbreaks, incrementally introducing malicious instructions. GAF's Context Layer, by maintaining session history and role awareness, detected the escalating intent. It identified patterns similar to Echo Chamber and Crescendo attacks, terminating the conversation mid-generation and logging the event for audit. This prevented a potential data breach and ensured compliance.
Outcome: GAF successfully prevented data exfiltration by detecting subtle, evolving threats that traditional WAFs would miss.
Estimate Your Potential AI Savings
Utilize our advanced ROI calculator to understand the financial impact of securing your generative AI applications with a robust GAF implementation.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Generative AI Security Roadmap
Charting a clear path to comprehensive GAF implementation, from foundational controls to advanced, context-aware protection.
2-Star Baseline (Mid-size Enterprise Chatbot)
Deploy GAF inline with identity integration, enforce roles/tool scopes, add rate limiting and basic syntactic validation, enable request/decision logging, and build a red-team corpus.
Path toward 5-Star (Regulated Institution)
Add semantic filters and selective redaction, adopt streaming cut-off for policy violations, integrate a context monitor to detect escalation across turns, with human-in-the-loop escalation, expand attack test suites, and link controls to governance requirements and audit reporting.
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