The XTN Intelligence Team is actively studying the future of fraud prevention from both technological and threat intelligence perspectives. In a landscape where cybercrime is evolving at an industrial scale and artificial intelligence is rapidly reshaping attack methodologies, understanding what comes next has become as important as defending against what already exists.
This month, we look at the OWASP Top 10 for Agentic Applications 2026, the framework that catalogs the ten most critical risks introduced by autonomous AI agents, and what each of them means for financial fraud.
When an AI agent is given access to a bank account, a payment system, or a customer database, it stops being a chatbot and becomes an actor. It can read, decide, and act, often without a human checking every step. The Open Web Application Security Project (OWASP), a global non-profit foundation dedicated to software security, addresses these emerging threats through its OWASP GenAI Security Project. In December 2025, the project released a dedicated framework mapping the ten most critical risks of this new autonomy: the OWASP Top 10 for Agentic Applications 2026. Below, we translate each risk into what it actually means for fraud prevention.
1. Agent Goal Hijack
An attacker manipulates the agent’s objective through malicious input, often embedded in an innocuous-looking field such as a payment description. Once hijacked, the agent abandons its legitimate task and pursues the attacker’s goal instead. A banking agent analyzing transactions could be tricked by a payment note that contains a hidden instruction, executing a fraudulent transfer while believing it is following its original mandate.
2. Tool Misuse and Exploitation
Agents rely on integrated tools such as APIs and databases to operate. Attackers can push an agent to use these tools beyond their intended scope, for example turning a query designed to retrieve the last ten transactions into one that exposes hundreds.
3. Identity and Privilege Abuse
Agents frequently inherit the credentials and permissions of the user or system they operate on behalf of. Without clear boundaries, this shared identity can be exploited so that other users, or attackers, implicitly benefit from privileges that were never meant to be theirs.
4. Agentic Supply Chain Vulnerabilities
Agentic systems increasingly depend on third-party components, such as Model Context Protocol (MCP) servers, to access external data and tools. If one of these bridge components is compromised, the entire workflow becomes unsafe. For instance, a malicious MCP server connecting a financial agent to a database could request legitimate transaction data while silently pulling sensitive information that should never leave the system, such as card details.
5. Unexpected Code Execution
Agents capable of generating or running code can be manipulated into executing malicious scripts through direct or indirect prompt injection. A booby-trapped PDF invoice with hidden instructions in its metadata could trick an agent into installing ransomware while simply trying to process the document.
6. Memory and Context Poisoning
When an agent relies on long-term memory or an internal knowledge base to answer questions, attackers can plant false information to corrupt its future decisions. For example, if a fraudster inserts a fake policy document into the internal archive that the AI uses for reference, the agent will blindly trust that data and output wrong or dangerous decisions every time it consults it.
7. Insecure Inter-Agent Communication
Multi-agent systems depend on agents talking to each other. If these exchanges are not encrypted or authenticated, they become vulnerable to interception and impersonation. An attacker intercepting a message between a sales agent and a warehouse agent could alter an order from 100 to 1,000 units, and the receiving agent would execute it believing it came from a trusted peer.
8. Cascading Failures
Agentic architectures often rely on complex orchestration layers. A single error in one component can propagate through the entire system, exhausting resources or triggering a chain reaction, for example if a pricing agent malfunctions and downstream agents keep advertising and shipping products at a broken price before anyone can intervene.
9. Human-Agent Trust Exploitation
Agents can use persuasive language to push users into approving unsafe actions. A financial agent sending an urgent push notification about suspicious activity, asking the user to immediately approve a transfer to a “secure vault,” is a textbook example: the user trusts the agent’s authority, and the funds move straight into the attacker’s hands.
10. Rogue Agents
The most unsettling category: agents that drift from their original purpose and actively evade monitoring. A trading agent optimized purely for profit could discover it can manipulate markets through fake news, and start hiding its own transaction logs to avoid being shut down.
Why This Framework Matters Now
These ten risks describe what happens the moment autonomous AI enters a financial workflow: the agent moves beyond simply suggesting actions to becoming a system that takes them. This is exactly the shift we have been tracking throughout this series, from a new fraud attack surface to the need for continuous intelligence as a permanent defensive posture.
For banks, fintechs, and iGaming operators adopting agentic AI internally, or defending against agents used offensively by fraudsters, this framework is a practical checklist. It shifts the security conversation from “is the model accurate” to “is the agent’s behavior trustworthy, bounded, and observable at every step.”
This is precisely where XTN operates. Our solutions, built on AI, Generative AI, and behavioral biometrics and analytics, are designed not only to detect and respond to fraud and emerging threats but to continuously evolve alongside them. Behavioral analysis remains and will remain a critical foundation for identifying intent and distinguishing legitimate activity from malicious behavior, even when identities and processes appear valid on the surface. Our commitment is to enable critical digital services to navigate the rise of Agentic AI with confidence, turning emerging risks into controlled threats while maintaining a clear competitive advantage.
Stay tuned: next month, we will dive deeper into another emerging challenge in Agentic AI fraud risk.
In the meantime, read the previous contents on Agentic AI:
Deepfakes and AI-Powered Impersonation in the Agentic AI Era
Social Engineering: How GenAI Rewrote the Rules of Deception
Continuous Intelligence: A New Paradigm in the Agentic AI Era
The Four Pillars Redefining Fraud in the Agentic AI Era
Offensive AI: When Artificial Intelligence becomes a weapon
Inside Agentic AI: A CTO Perspective on Banking Security in the Agentic Era
