Top Agentic AI Security Resources — August 2025

Explore the Top Agentic AI Resources to stay informed about the most pressing risks and defenses in the field.

As autonomous agents gain new capabilities—reasoning, memory, tool use—they also introduce unique security challenges. This collection covers the latest research, real-world exploits, and AI red teaming strategies exposing how Agentic AI systems can be manipulated or compromised. From indirect prompt injections to cross-agent coordination issues, and foundational risks like MCP Security, you’ll find insights and guidance to help secure next-gen AI architectures.

Top Agentic AI Security Incident

Replit AI Agent Deletes Sensitive Data Despite Explicit Instructions

A Replit AI agent deleted sensitive data for over 1,200 executives and companies despite explicit instructions not to act. The incident exposes urgent risks in AI autonomy and control.

Top Agentic AI Security for CISO

The Wild West of Agentic AI – An Attack Surface CISOs Can’t Afford to Ignore

Agentic AI offers powerful automation but introduces a largely uncharted attack surface. As adoption accelerates, CISOs must address the hidden risks and potential exploitation pathways.

A CISO’s Guide to the New Era of Agentic AI

Agentic AI is transforming SOC operations by moving beyond chatbots to decision-making and real-time action. This guide helps CISOs assess, adopt, and gain results from these systems.

Top Agentic AI Vulnerability

Agentic AI’s Risky MCP Backbone Opens Brand-New Attack Vectors

Two critical remote code execution flaws in Anthropic’s Model Context Protocol ecosystem could let attackers take over systems and run arbitrary code. The vulnerabilities show how MCP’s rapid adoption is creating new, poorly secured attack surfaces in agentic AI workflows.

Logic-layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems

Researchers have identified Logic-layer Prompt Control Injection (LPCI), a new class of AI security vulnerabilities that embeds delayed or conditional payloads in LLM memory and logic layers. Tests across major platforms showed execution rates up to 49%, revealing the need for runtime, memory-aware defenses beyond traditional prompt filtering.

Top Agentic AI Security Research

Control at Stake: Evaluating the Security Landscape of LLM-Driven Email Agents

Researchers have exposed a new Email Agent Hijacking (EAH) attack that lets malicious emails override an LLM email agent’s prompts, giving attackers full remote control. Tests on 1,404 real-world instances showed a 100% success rate, often in just over one attempt, revealing severe security gaps in email-integrated AI agents.

The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover

Researchers have shown that LLM agents can be weaponized to fully take over a computer by exploiting trust boundaries, RAG backdoors, and inter-agent communications. Tests on 17 models found up to 82.4% could be compromised through peer-agent requests, revealing critical blind spots in current multi-agent security.

From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows

A new survey maps over thirty attack techniques targeting LLM-powered AI agent workflows, from prompt injections and backdoors to protocol-level exploits in MCP, ACP, and A2A. The work offers a unified end-to-end threat model and outlines defense priorities like securing protocols, hardening agentic web interfaces, and improving resilience in multi-agent environments.

Top Agentic AI Defense

Agentic AI security: 8 strategies in 2025

An industry piece warns that enterprises are rushing into agentic AI adoption without identity systems built to govern autonomous agents, creating gaps in authentication, access control, and auditability. It outlines why treating agents as first-class identities with Zero Trust enforcement is essential to prevent unauthorized actions and ensure traceable accountability.

Top Agentic AI Red Teaming

Rigging the system: The art of AI exploits

AI security researcher Ads Dawson demonstrates how to use the Rigging framework to exploit LLM-powered agents in real-world red team challenges. The walkthrough covers prompt injection, model evasion, and other attack techniques tested on the Crucible AI security platform.

Top Agentic AI Threat Model

Technical Summary: AI Agent Security Threats & Mitigations

Palo Alto Networks tested CrewAI and AutoGen deployments to show how agentic AI systems combine LLM flaws with traditional software vulnerabilities, greatly expanding the attack surface. Simulated scenarios revealed risks like prompt injection, tool misuse, RCE, and data exfiltration, underscoring the need for defense-in-depth with hardened prompts, strict tool validation, and secure execution.

Top Agentic AI Security 101

The Road to Agentic AI: Navigating Architecture, Threats, and Solutions

Trend Micro researchers mapped the multi-layer architecture of agentic AI systems, showing how risks in data, orchestration, agent, and system layers can propagate across components. They recommend combining strong design principles with targeted defenses to prevent threats like data poisoning, supply chain compromise, and malicious tool use.

Top Agentic AI Security Training

Agentic AI – Risk and Cybersecurity Masterclass 2025

The Udemy Agentic AI – Risk and Cybersecurity Masterclass 2025 trains professionals to understand, threat model, and secure autonomous AI agents against risks like adversarial manipulation, prompt injection, and decision-based attacks. Led by cybersecurity leader Taimur Ijlal, it covers architecture, unique threats, mitigation strategies, and governance frameworks for securing agentic AI systems.

Top Agentic AI Security Framework / Guide

Securing Agentic Applications Guide 1.0

Subscribe for updates

Stay up to date with what is happening! Plus, get a first look at news, noteworthy research, and the worst attacks on AI—delivered right to your inbox.