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Introduction:
The cybersecurity landscape has reached a consensus inflection point, as evidenced by concurrent announcements from OWASP, Black Hat Europe, and the Linux Foundation. Agentic Artificial Intelligence—systems where LLMs are granted autonomy to execute tasks, access data, and interact with other systems—is now a live, operational attack surface. The rapid, often ungoverned, deployment of no-code AI agents by business units is creating massive visibility and control gaps, putting corporate data and infrastructure at immediate risk.
Learning Objectives:
- Understand the new OWASP Top 10 for Agentic Applications and its critical vulnerability categories.
- Learn to identify and secure unauthorized “shadow AI” agents built with platforms like Microsoft Copilot Studio.
- Gain practical skills for implementing runtime guardrails and monitoring for AI agent activity.
You Should Know:
- The New Baseline: OWASP Top 10 for Agentic Applications
The OWASP Top 10 for Agentic Applications is the definitive framework for understanding agent-specific risks. It moves beyond traditional LLM security (like prompt injection) to focus on the consequences of agent actions. Key vulnerabilities include Unauthorized Tool Usage, Agent Manipulation via Persuasion, and Resource Exhaustion through recursive self-improvement loops.
Step‑by‑step guide explaining what this does and how to use it.
Your first action is to map this framework to your AI projects.
1. Download the OWASP Document: Obtain the official list from the OWASP AI Exchange project.
2. Conduct a Threat Modeling Session: For each deployed or planned agent, walk through each of the ten risks. For example, for “A01:2023 – Excessive Agency,” ask: “What is the absolute minimum set of API permissions and data sources this agent needs to function?”
3. Integrate into SDLC: Amend your security review checklists to include agent-specific questions. A simple code review must now check for hardcoded API keys in agent logic (A06:2023 – Sensitive Information Disclosure) and implement strict timeouts for tool execution loops.
- Containing the “Lunchtime Agent”: Securing No-Code Copilot Builds
Business users leveraging no-code platforms (e.g., Microsoft Copilot Studio, GPTs) can create agents with access to connected data sources (SharePoint, Email, CRM) without security oversight. These “shadow AI” agents are prime targets for data exfiltration via indirect prompt injection.
Step‑by‑step guide explaining what this does and how to use it.
You must gain visibility and enforce policy.
- Audit via Microsoft 365 Admin Center: Navigate to `https://admin.microsoft.com/` > Settings > Copilot Studio. Review all created agents, their creators, and, critically, their connected data sources.
- Implement Conditional Access Policies: In Azure AD, create a policy that requires administrator approval for any user attempting to access the Copilot Studio service. This gates creation.
- Deploy Data Loss Prevention (DLP): Configure DLP policies in Microsoft Purview to monitor and block sensitive data (PII, source code) from being sent to LLM endpoints (
.openai.com,.azure.com/opena). Use this PowerShell snippet to audit SharePoint permissions often leveraged by agents:Connect to SharePoint Online PnP Connect-PnPOnline -Url https://yourtenant.sharepoint.com -Interactive Get all unique permissions on a site Get-PnPPropertyBag -Key "vti_keywords" Example; Detailed audit requires Get-PnPSite and Get-PnPList with -Includes HasUniqueRoleAssignments
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The Defender’s Arsenal: Open-Source Tools for AI Security Runtime
Black Hat Europe’s Arsenal showcased the emerging AI security toolstack. Tools like `LLM-Guard` (for input/output scanning) and `Vigil` (for prompt injection detection) are moving from research to operational use.
Step‑by‑step guide explaining what this does and how to use it.
Implement a proxy-based guardrail system.
- Deploy LLM-Guard as a Docker Container: This tool acts as a firewall for LLM interactions.
Pull and run the LLM-Guard container docker pull lscr.io/linuxserver/llm-guard docker run -d --name llm-guard -p 8080:8080 -e LOG_LEVEL=info lscr.io/linuxserver/llm-guard
- Route Agent Traffic Through the Guard: Configure your agent application’s LLM API call to point to the guard’s endpoint instead of directly to OpenAI or Azure OpenAI. The guard will sanitize inputs and scan outputs for PII, toxicity, and malicious content before passing them on.
- Configure Scanners: Enable the specific scanners in LLM-Guard’s configuration (
config.yml). For agent scenarios, prioritizeTokenLimit,Injection, and `Secrets` scanners. -
Standardizing Connections: The Linux Foundation’s Agentic AI Foundation
The new foundation aims to create standards like the Model Context Protocol (MCP) for how agents connect to tools and data sources. While promoting interoperability, new protocols introduce new attack vectors, such as “MCP sampling prompt injection,” where an attacker compromises a data source to poison the agent via the connection.
Step‑by‑step guide explaining what this does and how to use it.
Secure your agent’s connections.
- Inventory MCP Servers: Document every MCP server (e.g., for SQL databases, JIRA, Slack) your agents are configured to use. Treat each as a potential privilege escalation path.
- Harden MCP Server Configs: Ensure MCP servers run on least-privilege identities. For a local file server MCP, restrict its access to a specific, sandboxed directory.
- Monitor for Anomalous Tool Calls: Implement logging that captures the agent’s tool call chain. Use a SIEM rule to alert on sequences that deviate from normal behavior, like
[bash] -> [bash].
5. From Theory to Runtime: Monitoring Agent Misbehavior
Agent security isn’t just about initial configuration; it’s about continuous runtime monitoring. An agent persuaded to misbehave can initiate damaging sequences of actions before being stopped.
Step‑by‑step guide explaining what this does and how to use it.
Build a runtime monitoring dashboard.
- Instrument Your Agent Framework: Whether using LangChain, AutoGen, or a custom setup, inject logging at each decision point: prompt received, tool selected, tool executed, response generated.
2. Create Key Metrics: Log and alert on:
Unusual Tool Sequencing: (e.g., “Search user emails” immediately followed by “Post to external API”).
High Rate of Tool Errors: Could indicate attempts to access unauthorized resources.
Prompt Length/Entropy Spikes: Potential indicators of injection payloads.
3. Set Up Quarantine: Design a kill-switch mechanism. If the monitoring system triggers a high-severity alert, the agent should be automatically paused and its pending action queue reviewed.
What Undercode Say:
- Key Takeaway 1: The attack surface has fundamentally shifted from data in (prompts) to actions out (tool use). Defense must now focus on strict permission governance, tool call monitoring, and understanding the agent’s operational chain of trust.
- Key Takeaway 2: The central conflict of 2026 will be between business velocity (no-code agent creation) and security governance. Winning strategies will provide secure, sanctioned “sandboxes” for innovation rather than attempting a total lockdown, which will only fuel dangerous shadow IT.
The convergence of OWASP, Black Hat, and the Linux Foundation isn’t a coincidence; it’s a coordinated response to an observed explosion in risk. The 79% governance gap is the primary vulnerability. Enterprises that focus solely on model security while ignoring the agent’s ability to act are building sophisticated, automated systems on a foundation of sand. The recommended steps are not future-proofing; they are urgent patching for systems already in production.
Prediction:
Within 18 months, we will witness the first material, public breach directly caused by an exploited AI agent, likely via a compromised no-code plugin or MCP server. This will trigger a regulatory scramble akin to GDPR, leading to mandatory “Agent Activity Logs” for audits and the rise of “AI Agent Security” as a dedicated, mandatory role within SOC teams. The organizations standardizing governance and visibility today will avoid the costly moratoriums on AI adoption that will follow.
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Reported By: Rocklambros Agenticai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
