Ethical Disclosure:
The technical walkthroughs and conceptual PoCs provided in this article are for educational and architectural analysis purposes only. They are designed to illustrate structural semantic vulnerabilities and do not provide functional exploit code for any specific target or service. My goal is to facilitate a constructive discussion on building more resilient authorization models.

Abstract

Modern systems have significantly strengthened authentication mechanisms through biometrics, Passkeys, and hardware-backed security modules. However, many architectures still rely on an implicit assumption:

If authentication succeeds, subsequent actions are legitimate.

This paper examines a structural flaw I refer to as the Authorization Gap — a semantic disconnect between authenticated identity and verified intent.

We provide:

• A technical walkthrough of a Logic Mapping Attack
• An analysis of how AI automates semantic exploit discovery
• A structural defense model: Intent Lock

Importantly, this attack does not break encryption, bypass authentication, or escalate privileges illegally.
It exploits semantic misbinding within authorized flows.

1. Threat Model

Assumptions

• Identity verification succeeds.
• The attacker does not break cryptography.
• The attacker does not bypass authentication.
• The attacker does not exploit memory corruption.
• The system operates as designed.

The attacker instead exploits a semantic mismatch between:

• What the user believes they are authorizing
• What the system actually executes

We define this mismatch as:

Authorization Gap
A structural failure to bind user-visible intent to backend authorization semantics.

4. AI Acceleration of Semantic Exploit Discovery

AI does not introduce new logic flaws.
It accelerates the discovery of existing ones.
AI does not merely automate human tasks;
it operates as a semantic multi-tool that can identify architectural contradictions invisible to human observers.

4.1 Cross-Silo Semantic Correlation (Target Discovery)

In large-scale systems, UI/UX teams, backend developers, and documentation writers often operate in silos. This fragmentation creates “Semantic Drift” — small inconsistencies between what is promised to the user and what the code executes.

• How AI Finds It: By concurrently ingesting thousands of pages of API references, UI string files, and user manuals, an LLM can identify “islands of inconsistency”.
• The Exploit: AI identifies a prompt like “Help protect your account” mapped to a backend function authorize_third_party_access(). While a human ignores this nuance, AI marks it as a high-value Authorization Gap target.

4.2 Automated State-Space Mining

Humans are biased toward “happy paths” — the intended user flows. AI, however, is an expert at Boundary Value Analysis on a semantic scale.

• How AI Finds It: AI agents can perform “Fuzzing for Meaning,” where they systematically manipulate input parameters to observe how backend state transitions diverge from UI descriptions.
• The Exploit: AI discovers that if a specific sequence of “Cancel” and “Confirm” operations is executed in a specific timing, the system enters an Open Agency state — where authorization remains valid but user-visible context has been reset.

4.3 Detection of Logic Vulnerability Chaining

AI excels at connecting individually “safe” logical flaws into a catastrophic sequence.

• How AI Finds It: AI views CVEs and technical specifications not as isolated bugs, but as a Feature Catalog for building attack paths.
• The Exploit: It identifies that Operation A (correctly authorized) changes a metadata flag that Operation B (also authorized) fails to re-validate, ultimately allowing Operation C (high-risk) to execute without the intended user consent. This chain transforms a minor “Semantic Drift” into a Logic Mapping Attack.

4.4 Multi-Step Authorized Chaining

AI can combine individually legitimate operations:

Operation A (allowed)
Operation B (allowed)
Operation C (allowed)

Into a composite high-risk outcome unintended by system designers.

Each step is authorized.
The final result is not.

This is Authorization Gap exploitation at scale.

This pattern is not limited to telecom or IVR systems.
It appears in modern hardware-backed authentication ecosystems as well.

5. Structural Analogy: iOS Trust Model

A similar structural pattern exists in certain device authentication architectures.

Example:

• Face ID → Secure Enclave-backed biometric verification
• Passcode → Same privilege escalation authority
• Apps cannot distinguish the authentication source

In iOS LocalAuthentication:

LAPolicyDeviceOwnerAuthentication

Allows biometric authentication with automatic passcode fallback.

From the app’s perspective:

Success == trusted

But the source of trust may differ:

• Secure Enclave biometric match
• 6-digit passcode fallback

If high-assurance operations treat these as equivalent,
an Authorization Gap may emerge.

The system verifies Identity.
It does not bind Intent to authentication modality.

// Vulnerable: High-assurance action without source check
context.evaluatePolicy(
    .deviceOwnerAuthentication,
    localizedReason: "Confirm this high-risk action"
) { success, error in
    if success {
        // Gap: Biometric? Passcode? Unknown.
        // The app proceeds without knowing the "source of trust".
        executeHighRiskAction()
    }
}