Lets start with what is an Agentic Application?
Agentic applications are workflow-driven, memory-enabled AI systems where a large language model (LLM) doesn’t just answer questions—it:
- Plans and executes actions across steps
- Uses tools/APIs (e.g., search, databases, emails, Slack)
- Retains memory across sessions or users
- Interacts with humans or other agents
- Modifies environments (code, systems, infra) semi-autonomously
Think: copilots that schedule meetings, write code, update CRM, and order coffee… all at once.
Now think: what happens when they go rogue?
Agentic AI ≠ Just Chatbots. These are autonomous decision-makers with memory, tools, and authority. Without guardrails, they’re like interns with root access and a Red Bull addiction.
What the Guide Covers (Professionally Speaking):
- Secure design patterns for agentic architectures.
- Lifecycle guidelines for secure development, deployment, and runtime.
- Use cases, case studies, and real-world threat mappings (aka: what can go horribly wrong).
- Alignment with OWASP Top 10 for LLMs & Red Teaming for agent-based systems.
Agent Anatomy – The 6 Body Parts of Every Agent
- LLMs
- Orchestration
- Planning
- Memory
- Tools
- Environment
Threat Taxonomy (T-Codes)
The guide ties directly into the ASI Threat & Mitigation framework, categorizing attacks like:
- T1: Memory Poisoning
- T2: Tool Misuse
- T3: Privilege Compromise
- T5: Cascading Hallucination
- T6: Goal Hijacking / Intent Manipulation
- T9: Identity Spoofing
- T11: Remote Code Execution (RCE)
- T13: Rogue Agent Behavior
The goal: map threats → components → defenses.
Secure Architectural Patterns
Includes secure design for:
- Sequential agents (simple planner → executor pipeline)
- Hierarchical agents (one orchestrator agent assigning sub-tasks to specialized agents)
- Swarm / distributed agents (peer-to-peer collaboration among agents)
- Reactive agents (based on external stimuli like webhooks or events)
- Knowledge-intensive agents (RAG + memory-heavy)
Includes visual diagrams, components, tool integration models, and control flows.
Developer Lifecycle Guidance
Security across three development stages:
Secure Design Phase
- Threat modeling agentic systems
- Secure prompt engineering (e.g., use delimiters, allowlists, persona enforcement)
- Memory design (scoping, encryption, TTL, validation)
- Human-in-the-loop (HITL) placement
- Structured output enforcement (JSON schemas, validation pipelines)
- Input sanitization & multilingual safety
Secure Build & Deployment Phase
- Code scanning (SAST, secret detection, policy-as-code)
- Dependency analysis (SBOM/SCA, using tools like Snyk, pip-audit, GitHub Dependabot)
- Environment isolation (containers, VMs, Wasm, sandboxed interpreters)
- Just-in-time access, ephemeral tokens (OAuth, STS, signed JWTs)
- Separation of data and control planes (for multi-agent setups)
Secure Operations Phase
- Logging & observability (LangSmith, Langfuse, MLFlow)
- Tool and plan monitoring (rate limits, plan validation, anomaly detection)
- Guardrail integration (prompt injection detection, output filtering, PII redaction)
- Secure rendering & UI sanitization (DOMPurify, CSP headers)
- Memory scoping, TTLs, auto-expiry, audit trails
- Runtime alerting, forensics, SIEM integrations
Enhanced Security Strategies
- Tailored controls for: Memory isolation layers and Controlled access to sensitive tools (e.g., payment APIs, system files)
Recommended Use Cases for the Guide:
- Design-time reference for secure architecture reviews
- Security training for AI dev teams
- Threat modeling template for GenAI projects
- Integration checklist for LLM frameworks like LangChain, CrewAI, AutoGen
- Audit preparation for AI/LLM systems in regulated environments
“An agent isn’t dangerous because it’s smart. It’s dangerous because it thinks it knows better than you.”
Checkout - Securing Agentic Applications Guide [OWASP Gen AI Security Project - Agentic Security Initiative] - https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/
Also checkout the youtube video around it -