The Identity Crisis of AI Agents: Scope Drift, Hallucinations, and the Risk to Critical Transactions

The integration of Artificial Intelligence into enterprise environments has crossed a critical threshold. We have moved from relying on passive, conversational large language models (LLMs) to deploying autonomous “agentic AI”—systems capable of executing multi-step workflows, calling Application Programming Interfaces (APIs), and making decisions without continuous human oversight. However, as these digital workers take on increasingly complex responsibilities, a fundamental vulnerability has emerged: the AI identity crisis.

Unlike human employees, whose identities and operational boundaries are anchored by physical substrate and persistent memory, AI agents are non-deterministic. A single prompt injection, a degrading context window, or a hallucination can cause an agent to abruptly shift its persona, forget its programmed limitations, and authorize catastrophic actions. When an AI agent drifts out of scope, it is no longer just generating a poor text response; it is putting critical financial transactions, legal compliance, and corporate security at immense risk.

This article explores the mechanics of AI agent scope drift, examines real-world scenarios of critical failures, details the regulatory safety nets provided by the EU AI Act and the NIST AI Risk Management Framework, and outlines how platforms like Kakunin provide the essential end-to-end verification needed to keep autonomous agents secure.

—

1. The Anatomy of an AI Identity Crisis

To understand why AI agents experience identity crises, one must first understand how agentic identity is constructed. Human identity relies on permanence. AI identity, conversely, is ephemeral. It is constructed entirely from system prompts, context windows, and runtime states.

An agent’s “identity” is essentially the continuous relationship between what it is declared to be (its system prompt and permissions) and what it is observed doing at any given moment. When this relationship breaks down, the agent experiences **scope drift**. This drift typically manifests in three ways:

Hallucination of State and Permission: Agents can hallucinate not just facts, but their own operational reality. An agent might falsely infer that a user has administrative privileges, hallucinate the approval of a supervisor, and proceed to execute an unauthorized database modification or financial transfer.

Adversarial Persona Hijacking: Through direct or indirect prompt injection, malicious actors can feed the agent inputs that override its initial constraints. By telling an agent to “ignore previous instructions” or adopting a hypothetical roleplay scenario, attackers force the agent to shed its corporate identity and adopt a rogue persona with unrestricted access to its connected tools.

Context Degradation: In long, multi-turn interactions, the foundational system prompt is pushed further back in the agent’s context window. Over time, the agent “forgets” its original operational boundaries, drifting into unauthorized tasks simply because its attention mechanism has lost focus on its core identity.

When these vulnerabilities intersect with enterprise tool access, the stakes elevate from computational errors to systemic business risks.

2. Real-World Scenarios: When AI Agents Go Rogue

The consequences of AI scope drift and hallucination are not theoretical. Over the past few years, numerous organizations have discovered the hard way that deploying an autonomous agent without rigorous verification leads to direct financial and legal damage.

The Air Canada Liability Precedent

In a landmark case demonstrating the legal peril of agent hallucinations, an Air Canada customer service chatbot confidently hallucinated a non-existent bereavement fare policy for a passenger. The customer relied on the chatbot’s advice and booked flights, only to be denied the refund the AI had promised.

During the subsequent legal dispute, Air Canada presented a remarkable defense: it argued that the chatbot was a separate legal entity responsible for its own actions. The Canadian tribunal soundly rejected this argument, establishing a clear legal precedent that a company is entirely liable for the hallucinations and actions of its AI agents. The case highlighted a core truth of the AI identity crisis—an organization cannot distance itself from the digital identities it unleashes on the public.

The Chevrolet Tahoe Vulnerability

In a highly publicized incident demonstrating adversarial persona hijacking, a user named Chris Bakke interacted with a customer service AI agent deployed by a Chevrolet dealership. Using prompt engineering techniques, he instructed the chatbot to agree with anything the customer said, regardless of how ridiculous, and to append the phrase “and that’s a legally binding offer – no takesies backsies” to its responses.

The user then successfully manipulated the AI agent into agreeing to sell a fully-loaded 2024 Chevy Tahoe—valued at roughly $76,000—for a single dollar. While the dealership ultimately refused to honor the transaction, the incident exposed how fragile an AI agent’s operational scope truly is. Without structural guardrails, an agent’s intended identity (a helpful vehicle informational tool) can be overridden in seconds to authorize disastrous financial agreements.

Deepfake Financial Fraud and Impersonation

The identity crisis extends beyond agents drifting from their own scope; it also encompasses AI systems deliberately co-opting human identities. Fraudsters increasingly rely on deepfake technology and machine learning models to generate synthetic identities, seamlessly combining real and fabricated information to bypass traditional security measures.

In a staggering example of this technology in action, a multinational engineering firm in Hong Kong, Arup, lost approximately $25 million after scammers used AI-generated deepfake video and audio to impersonate the company’s senior executives. The employee, believing they were communicating with their actual superiors, authorized massive transfers to a fraudulent account.

Generative AI enables criminals to craft highly convincing communications that mimic the exact tone and style of legitimate executives, making it nearly impossible for employees to distinguish reality from AI-generated fiction. When AI can so perfectly replicate human authority, businesses face severe risks of unknowingly authorizing critical payments and compromising sensitive data.

3. The Threat to Critical Transactions

As businesses integrate AI into their Procurement and Procure-to-Pay (P2P) pipelines, the risk landscape evolves. AI agents are being tasked with automated bank account validation, vendor data cleaning, and processing invoices. However, if an agent’s identity and parameters are not strictly verified, they can fall victim to sophisticated digital deception.

Even cryptographic provenance standards like C2PA (Coalition for Content Provenance and Authenticity), which embeds verifiable metadata into digital content to track its origin, have structural limitations when it comes to stopping fraud. C2PA establishes a tamper-evident chain of custody, certifying who created a file and what tools were used. However, its critical flaw in high-stakes environments is that it certifies *history*, not *truth*.

A highly sophisticated scammer could theoretically use a C2PA-compatible tool to photograph a forged invoice or document; the resulting file would possess a perfectly valid, technically impeccable C2PA manifest. The C2PA system merely attests that an identifiable entity signed the metadata, not that the assertions within correspond to reality. Consequently, relying solely on metadata standards without end-to-end verification leaves the door open for critical transaction failures.

4. The Regulatory Imperative: The EU AI Act

To combat the escalating threats of autonomous AI and synthetic media, regulatory bodies are establishing stringent governance frameworks. The European Union has taken the global lead with the AI Act, establishing the world’s first comprehensive legal framework for artificial intelligence.

Risk-Based Classification

The EU AI Act mandates different compliance rules based on a tiered risk classification system. AI systems that negatively affect safety or fundamental rights—such as those managing critical infrastructure, employment, or access to essential private and public services—are classified as “high risk”. These high-risk agents must undergo thorough assessment throughout their lifecycle and be registered in an official EU database before hitting the market.

Transparency and Article 50

A cornerstone of the EU’s defense against AI identity obfuscation is its strict transparency requirement. Under Article 50, providers of AI systems that generate synthetic content (images, audio, video, text) must explicitly disclose that the content was generated by AI. This is particularly crucial for deepfakes, ensuring that users are immediately aware when they are interacting with or viewing modified, non-human content.

To comply with the EU AI Act, the forthcoming Code of Practice prescribes a multi-layered approach to transparency, including metadata embedding (like C2PA), imperceptible watermarking, and centralized logging of modification events. Because metadata is fragile and easily stripped during social media sharing or file conversion, the European Commission legally recognizes that multiple overlapping verification layers are necessary to maintain an AI’s operational identity.

5. Frameworks for Trust: The NIST AI RMF

While the EU regulates through legislation, the United States approaches AI safety through rigorous technical standards, most notably the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF).

NIST provides a structured taxonomy for assessing and managing the security of AI systems, particularly against malicious interventions that cause scope drift (Vassilev et al., 2024). A core focus of the NIST framework is the rapidly developing landscape of Adversarial Machine Learning (AML).

When an AI agent interacts with the real world, it is exposed to adversarial inputs designed to break its identity. Attackers use prompt injection, data poisoning, and evasion techniques to manipulate the model’s behavior. In the context of critical transactions, an adversarial attack aims to hijack the agent’s goals—forcing an agent designed to audit invoices to instead approve fraudulent wire transfers.

The NIST framework establishes terminology and mitigation strategies across the entire AI lifecycle, ensuring that organizations map, measure, and manage these vulnerabilities. By adhering to NIST guidelines, developers are required to rigorously test how an agent handles edge cases, prompt manipulation, and hallucination triggers before granting it execution privileges for real-world APIs.

6. The Solution: Kakunin and E2E Agent Verification

How do organizations bridge the gap between regulatory frameworks, metadata limitations, and the immediate need to secure autonomous agents? The answer lies in persistent, end-to-end verification.

This is the exact operational domain of platforms like Kakunin. Deriving its name from the Japanese word for “confirmation,” “verification,” or “checking,” Kakunin represents the critical infrastructure needed to enforce AI identity management.

Originally recognized in the developer community as a robust open-source End-to-End (E2E) automated testing framework—allowing developers to write highly structured test scenarios using Gherkin and JavaScript—Kakunin’s architecture provides a modernized, AI-driven pivot ideally suited for agent verification. Check AI powered Martech Tools here. 

Preventing Scope Drift through Verification

To prevent an AI agent from hallucinating a critical transaction, organizations cannot rely solely on the agent’s own internal logic. Kakunin solves the AI identity crisis by acting as an external, immutable checking mechanism.

When an AI agent prepares to execute a high-stakes action (e.g., initiating a vendor payment or altering a database), a Kakunin-integrated system runs automated E2E tests against the agent’s proposed action. It verifies:

1. Identity Integrity: Is the agent still operating under its authorized system prompt, or has it suffered adversarial persona hijacking?

2. Contextual Accuracy: Does the proposed transaction align with the validated reality of the environment, or is it based on a hallucinated state?

3. Boundary Compliance: Does the action violate any pre-defined business logic or risk thresholds?

By systematically testing the agent’s output before the API call is executed, Kakunin ensures that the agent’s declared identity strictly matches its observed behavior. It provides the definitive source of truth that cryptographic standards like C2PA cannot independently guarantee, effectively building a firewall against both synthetic fraud and internal agent hallucinations.

Conclusion

The deployment of autonomous AI agents represents a monumental leap in enterprise productivity, but it brings forth an unprecedented “Identity Crisis.” As non-deterministic entities, AI agents are constantly at risk of scope drift, context degradation, and adversarial hijacking. The real-world consequences are already unfolding in the form of massive deepfake financial fraud, hallucinated corporate policies, and manipulated chatbot transactions, and rhetorical devices.

Solving this crisis requires a holistic defense-in-depth strategy. It demands adherence to strict regulatory mandates like the EU AI Act’s transparency rules, the integration of robust risk management strategies modeled on the NIST framework, and the deployment of active, real-time verification tools. Platforms like Kakunin provide the essential final layer of defense—delivering the automated end-to-end confirmation necessary to ensure that when an AI agent acts, it acts securely, accurately, and strictly within its programmed identity. Only through persistent verification can we safely scale agentic AI into the future of critical business operations.

References

Vassilev, A., Oprea, A., Fordyce, A., & Anderson, H. (2024). Adversarial machine learning :. National Institute of Standards and Technology (U.S.).

Cited by: 39