Securing Autonomous Agents, RAG & MCP Extensions
AgentClad Security provides engineering-grade threat modeling, adversarial red teaming, and guardrail architecture to assure safety across production agentic workloads.
Interactive Agentic Architecture Diagram
Autonomous AI Agents
Re-act loops, multi-step execution plans, and function call orchestrators.
Excessive autonomy, privilege escalation, memory context poisoning.
Least-privilege tool schemas, human-in-the-loop gates, turn limits.
Threat Landscape for Agentic Systems
Standard software vulnerabilities cannot account for probabilistic model behavior, prompt injection, and tool abuse. We target the top five vectors threatening enterprise AI deployments.
Direct & Indirect Prompt Injection
Manipulating model context via untrusted user inputs or retrieved documents.
Attackers embed adversarial instructions into user prompts or ingested RAG context (emails, PDFs, websites) to override system rules and hijack model intent.
Excessive Agent Autonomy & Tool Abuse
Agents executing high-impact actions without privilege boundaries or human approval.
Autonomous agents granted broad API permissions can be tricked into invoking sensitive tools, modifying production databases, or triggering unauthorized financial transactions.
Data Leakage & Context Poisoning in RAG
Cross-tenant knowledge base retrieval and vector store manipulation.
Insecure vector search queries allow malicious users to extract confidential internal documents, PII, or executive strategy memos from shared vector indexes.
Insecure MCP Extensions & Supply Chain
Vulnerable Model Context Protocol servers exposing local resources.
Unvetted MCP tools installed on host machines can expose path-traversal vulnerabilities, unauthenticated SSE transport streams, or shell injection flaws.
Unbounded Execution & Cascading Exploits
Recursive agent loops and inter-agent privilege escalation vectors.
Multi-agent ecosystems where one compromised agent invokes secondary agents with higher privileges, causing infinite execution loops and cascading system failures.
Six Pillars of AI Security Architecture
From pre-implementation threat modeling to continuous runtime monitoring and regulatory attestation.
Threat Modeling & Architecture Design
Identify trust boundaries and misuse vectors early.
Map agent components, model endpoints, tool capabilities, and memory stores to discover architectural flaws before deployment.
- AI Risk Register
- Trust Boundary Diagrams
- Abuse Case Catalogs
Pipeline & RAG Guardrail Hardening
Secure knowledge retrieval and context ingestion.
Implement strict document metadata filtering, PII redaction, and prompt injection filters ahead of vector embedding stores.
- RAG Metadata Filtering Blueprint
- Guardrail Rulesets
- Context Ingestion Scanners
MCP & Tool Extension Sandboxing
Isolate local tools, resources, and API integrations.
Enforce tight parameter validation schemas, containerized tool sandboxes, and secure transport protocols for MCP servers.
- MCP Security Baseline Specs
- Zod Tool Validators
- Sandbox Docker Blueprints
Adversarial Red Teaming & Jailbreaking
Stress-test model resilience under realistic attacks.
Execute automated fuzzing and expert manual attacks probing prompt injection, tool hijacking, and system prompt extraction.
- Red Team Audit Reports
- Jailbreak Matrix
- Proof-of-Concept Exploit Scripts
Runtime Monitoring & Observability
Detect agent anomalies and guardrail trips in real-time.
Configure structured logging, SIEM detection rules for tool calling spikes, and emergency kill-switch controls.
- AI Log Telemetry Spec
- SIEM Alert Rules
- Agent Kill-Switch Handlers
Governance, Compliance & Assurance
Align AI operations with global security standards.
Map security controls to ISO/IEC 42001, NIST AI RMF, and EU AI Act requirements with executive attestation reports.
- ISO 42001 Control Mapping
- EU AI Act Gap Assessment
- Executive Attestation Deck
High-Assurance AI Security Services
AI Red Teaming & Adversarial Testing
Identify jailbreaks, prompt injection vulnerabilities, function abuse, and non-deterministic logic flaws before attackers do.
Autonomous Agent Security Architecture Review
Verify principle of least privilege, memory persistence, tool execution sandboxing, and human-in-the-loop controls for autonomous agents.
Model Context Protocol (MCP) & Extension Security
Audit MCP tool definitions, resource schemas, prompt templates, and communication channels for remote code execution and data exfiltration.
LLM Pipeline & RAG Infrastructure Hardening
Prevent context poisoning, vector store leakage, unauthorized document retrieval, and guardrail evasion in RAG pipelines.
AI Threat Modeling & Risk Assessment
Map trust zones, model assets, tool interfaces, and failure scenarios early in the product design lifecycle.
SecOps & AI Governance Integration
Build continuous AI monitoring, audit logging, policy enforcement, and regulatory compliance readiness into your SOC.
Fictional Sample Deliverables & Audit Artifacts
Every engagement yields actionable engineering documentation and executive reports. Below are synthetic sample artifacts demonstrating our report quality.
Deliverables and reports showcased on this site are synthetic samples created for illustration purposes.
Adversarial AI Red Team Audit Report
Production Agentic Staging Endpoint
Executive summary, 18 high-confidence exploit vectors, jailbreak failure rates, system prompt vulnerability analysis, and re-test verification certificate.
MCP Server Security Audit & Sandbox Specification
Custom Model Context Protocol Tools
Path traversal vulnerability remediations, tool parameter validation wrappers, and secure transport (SSE/TLS) configuration specs.
RAG Knowledge Base Metadata Access Blueprint
Multi-Tenant Vector DB Pipeline
Document-level metadata tenant filtering blueprints, indirect prompt injection filter rules, and PII scrubbing pipeline integration.
Scope / Assess / Fix / Prove
Our high-assurance methodology moves from initial threat modeling to active adversarial testing, engineering remediation, and final verification proof.
Scope & Threat Model
We deconstruct your AI agent architecture, vector pipelines, MCP tool definitions, and trust boundaries during interactive scoping workshops.
- Scoping Document
- AI Threat Model & Trust Boundary Map
- Rules of Engagement
Assess & Adversarial Red Team
Our team executes automated fuzzing and expert manual red teaming against prompt interfaces, RAG context vectors, and MCP extensions.
- Red Team Assessment Report
- Jailbreak Vulnerability Matrix
- PoC Exploit Repro Scripts
Fix & Remediate
We partner directly with your engineering leads to deliver precise, production-grade security patches and guardrail rule configurations.
- Engineering Patch Guide
- Guardrail Configuration Files
- Tool Hardening Snippets
Prove & Re-Test
We re-execute test suites against remediated staging environments to verify exploit resolution and issue executive attestation reports.
- Verification & Re-Test Report
- Executive Security Attestation Certificate
- SecOps Log Rules
Is AgentClad Security the Right Fit?
We partner with security leaders and engineering teams who need deep technical rigor rather than checkbox compliance.
Ideal Engagement Partners
- Production Agent Deployments: Organizations shipping autonomous agents connected to live APIs, internal databases, or MCP tools.
- High-Assurance Industries: SaaS vendors, fintech, healthcare, and enterprise tech requiring strict data isolation and safety.
- Engineering-Driven Security Teams: CISOs and Product Security leads seeking actionable code patches and guardrail rules rather than generic PDF templates.
- Custom MCP & RAG Builders: Developers launching custom Model Context Protocol servers or multi-tenant vector retrieval pipelines.
Who We Are Not For
- Checkbox Compliance Only: Teams seeking automated-only scanning tools to fulfill a generic compliance badge without fixing root causes.
- Consumer Chatbot Wrappers: Simple single-prompt static text completion sites with no external tools or data connections.
- Physical & Network Pentesting: Organizations needing physical facility penetration testing or network firewall audits.
- Zero-Engineering Inquiries: Teams unwilling to assign engineering contacts for scoping or remediation verification.
25-Point Production AI Security Baseline Checklist
A practical, non-gated benchmark covering model security, agent autonomy boundaries, RAG data isolation, MCP extension sandboxing, and SecOps observability.
1. Model & Prompt Security
Defenses against prompt injection, system prompt extraction, and model manipulation.
2. Agent Autonomy & Permission Boundaries
Controls preventing excessive agent privilege, function abuse, and loop exhaustion.
3. Data Protection & RAG Infrastructure
Confidentiality and integrity safeguards for vector stores, embeddings, and context retrieval.
4. MCP & Extension Sandboxing
Hardening for Model Context Protocol (MCP) servers, tools, and local/remote integrations.
5. Monitoring, Incident Response & Governance
Audit logging, SOC observability, incident containment, and regulatory alignment.
AI Security Engagements & Technical FAQs
Clear answers regarding scoping, data confidentiality, non-deterministic testing, MCP auditing, and compliance alignment.
Traditional penetration testing focuses on network protocols, web application logic, authentication bypasses, and OS vulnerabilities. AI Red Teaming targets probabilistic model behaviors, prompt injection (direct and indirect), system prompt extraction, autonomous agent tool abuse, memory corruption across multi-turn sessions, and RAG context poisoning. It requires specialized adversarial techniques to probe model boundaries rather than standard static vulnerabilities.
Ready to Assure Your Production AI Agent Systems?
Schedule an initial technical scoping call with our AI security architecture leads to evaluate your agent workflows, MCP tools, and RAG pipelines.
Direct Security Inquiries: security@agentclad.ai