Hybrid AI Vulnerability Scanning (H-AVS)
This work package outlines a modern, AI-Native Vulnerability Management program for 2026. Traditional scanning is no longer sufficient for Agentic AI; this framework integrates specialized testing for Large Language Models (LLMs), Model Context Protocol (MCP), and autonomous agent logic across multi-cloud (AWS, Azure, GCP) and on-premise environments.
Objective: To implement a continuous "Code-to-Runtime" security validation pipeline using a 4-tier scanning architecture (SAST, DAST, IAST, MAST) tailored for AI-integrated enterprises.
Scans source code and AI configuration files for security flaws before they are compiled or deployed.
Logic & Prompt Leakage: Scan for "system prompt" leakage risks and hardcoded instructions that could be bypassed by users.
AIBOM Validation: Automatically generate and scan an AI Bill of Materials (AIBOM) to identify vulnerable base models (e.g., outdated Llama or Mistral weights) or insecure Python dependencies.
Insecure Tooling: Detect where AI agents are granted excessive permissions (e.g., an agent given full os.system access instead of a sandboxed environment).
Primary Tooling (2026): Snyk Agent Fix, Checkmarx One, or Semgrep Assistant.
Tests the running AI application from the perspective of an external attacker.
Adversarial Prompt Injection: Automated fuzzing of input fields to attempt Jailbreaks, Indirect Prompt Injections, and Goal Hijacking.
MCP Server Fuzzing: Testing the Model Context Protocol (MCP) endpoints for SSRF (Server-Side Request Forgery) and unauthorized tool execution.
Non-Deterministic Testing: Running multiple iterations of the same attack to account for the "probabilistic" nature of AI responses.
Primary Tooling (2026): Invicti (with AI Tool Scanner), Burp Suite Enterprise, or StackHawk.
Uses agents inside the application to monitor real-time data flows between the AI and internal databases.
Context Integrity Monitoring: Track how data retrieved from a Vector Database (RAG) is processed. IAST detects if sensitive data (PII/PHI) is accidentally "leaked" into the AI’s reasoning context.
Trust Score Validation: Validate that the AI’s internal "Trust Scores" are functioning—ensuring high-risk tools are only accessible when the model’s confidence is high.
Primary Tooling (2026): Contrast Security or Veracode IAST.
Specific to AI-powered mobile apps (iOS/Android) communicating with cloud-based LLMs.
Local Model Hardening: Scan on-device "Small Language Models" (SLMs) for weight extraction vulnerabilities.
Insecure API Handshakes: Verify that the mobile client uses Certificate Pinning and Short-lived JWTs when communicating with cloud AI gateways to prevent Man-in-the-Middle (MITM) attacks.
Deliverable: Mobile AI Threat Assessment Report.
Primary Tooling (2026): Quokka (Q-mast) or NowSecure.
Environment
Deployment Strategy
Multi-Cloud
Agentless Scanning: Use cloud-native APIs (e.g., Wiz or SentinelOne AI-SPM) to scan AI pipelines in AWS S3, Azure AI Foundry, and GCP Vertex AI without installing agents.
On-Premise
Air-Gapped Scanners: Deploy self-hosted instances of SAST/DAST (e.g., HCL AppScan) to scan proprietary models and sensitive local datasets that cannot leave the internal network.
Consolidated Vulnerability Dashboard: A single-pane-of-glass view (ASPM) integrating findings from all four scan types across all clouds.
AI Remediation Playbook: Automated "fix" scripts for common AI vulnerabilities (e.g., updating a system prompt to prevent jailbreaking).
Compliance Mapping: Documentation showing how these scans satisfy the EU AI Act and ISO 42001 requirements.
Would you like me to create a "Remediation Workflow" for a specific vulnerability, such as an AI agent being successfully "nudged" into exfiltrating corporate data?
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