Spotting Rogue Apps: AI-Powered Threat Detection in Microsoft Entra ID

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Author: Denis Avetisyan

Researchers have developed a new approach to identify potentially malicious applications registered within Microsoft Entra ID by analyzing permission requests and user consent patterns.

The comparative analysis of tri-gram similarity across large language models reveals nuanced distinctions in their reasoning processes when assessing permission-related risks, suggesting that while models may arrive at similar conclusions, the pathways they take to do so-and therefore their inherent vulnerabilities-can diverge significantly.
The comparative analysis of tri-gram similarity across large language models reveals nuanced distinctions in their reasoning processes when assessing permission-related risks, suggesting that while models may arrive at similar conclusions, the pathways they take to do so-and therefore their inherent vulnerabilities-can diverge significantly.

This research introduces a framework for scoring permission risk and detecting anomalous consent behavior using large language model-assisted analysis within the Microsoft Entra ID ecosystem.

Modern identity and access management systems, while robust, struggle to effectively differentiate between legitimate application behavior and malicious intent during the OAuth consent process. This challenge is addressed in ‘Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring’, which introduces a novel framework for automatically assessing the risk associated with permission requests. By leveraging large language models to score permissions and identify anomalous consent patterns, the research demonstrates a pathway toward real-time detection of potentially harmful applications within Microsoft Entra ID. Could this approach represent a significant step forward in proactive application security and consent management, ultimately reducing the attack surface for cloud-based resources?

The Expanding Web of Access: Permission Creep in the Cloud

Contemporary cloud applications are fundamentally built upon a system of delegated permissions, with OAuth serving as a dominant protocol for authorizing access to resources without sharing credentials. This reliance, while enabling seamless integration and user experience, inadvertently constructs an intricate network of access rights that spans multiple applications and services. Each permission granted represents a potential entry point for malicious actors, and the sheer scale of these connections-often numbering in the hundreds per user-creates a complex web difficult to monitor and manage. Consequently, a single compromised application can potentially unlock access to a vast trove of data across interconnected services, highlighting the critical need for granular permission control and continuous security assessment in the modern cloud environment.

The proliferation of cloud services has led to a staggering increase in the number of permissions granted to applications, creating a vastly expanded attack surface for malicious actors. While the principle of least privilege – granting only the minimum necessary access – is a cornerstone of security best practice, it is frequently disregarded in favor of convenience or broad functionality. This oversight results in applications possessing permissions far exceeding their operational needs, effectively providing attackers with numerous potential entry points should an application be compromised. Each unnecessary permission represents a potential pathway to sensitive data, and the sheer volume of these misconfigurations dramatically increases the likelihood of a successful breach, turning a minor vulnerability into a critical security risk. Consequently, organizations face a growing challenge in managing and auditing these permissions to effectively mitigate the expanding threat landscape.

Exploitation of cloud permission misconfigurations has become a primary vector for data breaches, as rogue applications and malicious actors actively probe for, and capitalize on, overly permissive access rights. Attackers frequently target applications with broad scopes, leveraging them as stepping stones to access sensitive data belonging to users or the organization itself. This often involves techniques like token theft, where legitimate authorization tokens are intercepted and abused, or exploiting vulnerabilities in the application’s permission handling logic. The resulting unauthorized access can range from data exfiltration and modification to complete account takeover, highlighting the critical need for robust permission management and continuous monitoring to detect and mitigate these increasingly prevalent threats.

Conventional security measures, designed for static network perimeters, are increasingly ineffective against the fluidity of cloud permissions and the evolving tactics of contemporary attackers. These legacy systems often struggle to monitor and manage the constant granting and revocation of access rights, creating blind spots that malicious actors readily exploit. Modern attacks frequently bypass traditional defenses by leveraging overly permissive configurations and exploiting the complex relationships between applications and data resources. The rapid scaling and ephemeral nature of cloud environments further compound the challenge, as security tools struggle to maintain visibility and enforce policies in a consistently reliable manner. This mismatch between established security paradigms and the dynamic cloud landscape necessitates a shift toward more adaptive and intelligent security solutions capable of proactively identifying and mitigating permission-based risks.

This framework detects rogue applications to enhance system security.
This framework detects rogue applications to enhance system security.

Semantic Scrutiny: LLM-Assisted Permission Analysis

Large Language Models (LLMs) represent a departure from traditional permission analysis techniques by enabling semantic understanding of permission descriptions. Instead of relying on keyword matching or pre-defined rule sets, LLMs process the natural language defining each permission to determine its potential impact. This allows for risk assessment based on the functionality a permission enables, rather than simply recognizing patterns in its name or associated metadata. The system analyzes permissions to identify potentially dangerous actions or data access, considering the context of the permission’s purpose. This approach facilitates a more nuanced and accurate evaluation of risk compared to static analysis methods.

LLM-Assisted Permission Analysis improves upon conventional risk scoring methods by moving beyond static analysis to dynamically evaluate permissions within their operational context. This is achieved by utilizing Large Language Models to interpret the stated purpose of each permission and correlate it with potential vulnerabilities. Traditional systems often rely on pattern matching or keyword identification, whereas this approach assesses the semantic meaning of a permission’s description to determine its actual impact. The LLM determines if the scope of a permission is excessive for its intended function, or if seemingly benign permissions, when combined, could be exploited. This contextual understanding enables a more nuanced and accurate assessment of risk than is achievable through purely signature-based or rule-based systems.

Traditional permission analysis often relies on pattern matching to identify potentially risky actions; however, this approach fails to account for the context and scope of each permission. Our research addresses this limitation by assessing the actual impact of permissions, specifically by analyzing factors such as the breadth of data access and the level of control granted. A comprehensive analysis was conducted on 769 Microsoft Graph permissions to determine risk levels based on these contextual factors, enabling a more nuanced understanding of potential vulnerabilities than simple signature-based detection methods provide. This granular assessment allows for prioritization of remediation efforts based on the actual potential for exploitation, rather than relying on generalized risk classifications.

The automated risk assessment framework significantly reduces the manual effort traditionally required for permission analysis, while simultaneously improving accuracy through consistent application of defined criteria. This proactive approach enables the identification of potentially exploitable, high-risk permissions before they are leveraged by malicious actors. The system categorizes each assessed permission into one of five risk levels – 1 through 5 – based on a combination of factors including scope, potential impact, and common attack vectors. This granular risk scoring allows security teams to prioritize remediation efforts and focus on the most critical vulnerabilities within the permission structure.

Analysis of bi-gram similarity reveals variations in how different large language models reason about permission risks.
Analysis of bi-gram similarity reveals variations in how different large language models reason about permission risks.

The Foundation of Access: Application Registration and Permission Scope

Application Registration within Microsoft Entra ID establishes the initial identity of an application and serves as the foundational step in defining its authorized access. This process involves providing metadata about the application to Entra ID, including its name, supported account types, and a redirect URI for handling authentication responses. Crucially, registration creates an application object within Entra ID, which is then used to manage the application’s security credentials and, most importantly, to define the permissions it requests. Without successful registration, an application cannot be authenticated or authorized to access protected resources within the Microsoft Entra environment, effectively preventing it from functioning as intended.

Application Registration in Microsoft Entra ID involves defining the permissions an application requires to function, categorized as Delegated Permissions and Application Permissions. Delegated Permissions allow an application to act on behalf of a signed-in user, limited by the user’s granted rights; these require user consent. Application Permissions enable an application to access resources directly, operating without a signed-in user and functioning with organizational-level permissions. The specific permissions requested during registration determine the application’s access scope, influencing its ability to read, write, or modify data and services within the Microsoft Entra environment. Proper configuration of these permissions is fundamental to the principle of least privilege and securing organizational resources.

Accurate risk assessment concerning application access within Microsoft Entra ID necessitates a detailed understanding of permission scope. Permissions can be granted at the tenant level, providing access to all resources within the organization, or be narrowly scoped to specific resources such as individual Microsoft Graph API endpoints or custom application data. Tenant-wide permissions, while simplifying development, represent a significantly higher risk profile due to the potential for broad data exposure or unauthorized actions. Conversely, resource-specific permissions limit the blast radius of a compromised application, but require more granular configuration and potentially increased complexity in application logic. Evaluating the principle of least privilege – granting only the necessary permissions for an application to function – is therefore critical when assessing and mitigating potential security vulnerabilities related to delegated or application permissions.

Offline Access permissions, specifically the offline_access scope requested during application registration in Microsoft Entra ID, allow applications to obtain refresh tokens, enabling continued access to protected resources even when a user is not actively signed in. While enhancing user experience, this functionality substantially expands the attack surface because a compromised refresh token can be used to impersonate a user indefinitely. Consequently, applications requesting offline_access should undergo rigorous security assessments, including code review and penetration testing, and their use should be limited to scenarios where continuous, unattended access is demonstrably necessary. Furthermore, administrators should closely monitor applications utilizing these permissions and implement appropriate conditional access policies to mitigate the risk of token misuse.

Defense in Depth: The Least Privilege Principle and Attack Vectors

The principle of least privilege stands as a foundational tenet of secure system design, dictating that every module – whether an application, user, or process – should operate with the minimal set of permissions necessary to complete its intended task. This isn’t simply about restricting access; it’s a proactive strategy to contain potential damage. By limiting an application’s reach, even if compromised by malicious actors, the blast radius of a security incident is dramatically reduced. A compromised application with limited permissions can only access a limited set of resources, preventing widespread data breaches or system-level control. This approach minimizes vulnerabilities and ensures that even successful attacks yield less impactful results, forming a critical layer of defense against evolving cyber threats and reinforcing overall system resilience.

Consent phishing represents a significant and increasingly prevalent attack vector, wherein malicious actors deceive users into voluntarily granting excessive permissions to seemingly legitimate applications. These attacks often involve sophisticated social engineering techniques, crafting convincing prompts or interfaces that request broad access to data and system resources under false pretenses. Once granted, these delegated permissions allow the rogue application to operate with elevated privileges, potentially enabling data breaches, unauthorized access to sensitive information, and further propagation of malicious activity. The success of consent phishing hinges on exploiting user trust and a lack of awareness regarding the scope of permissions requested, making it a particularly challenging threat to mitigate through technical controls alone; user education and proactive monitoring for suspicious permission requests are crucial defensive measures.

Misconfigured application permissions represent a significant vulnerability, frequently exploited by threat actors to achieve privilege escalation and compromise sensitive data. When applications are granted excessive permissions – beyond those strictly necessary for their function – attackers can leverage these privileges to access restricted resources, modify system configurations, or even install malicious software. This often begins with gaining control of a legitimate application account, then utilizing its elevated permissions to move laterally within a system, bypassing standard security controls. The resulting unauthorized access can lead to data breaches, financial loss, and reputational damage, underscoring the critical need for granular permission management and continuous monitoring to identify and rectify potentially dangerous configurations before they are exploited.

Effective security necessitates a forward-looking strategy that integrates automated analysis with the foundational principle of least privilege. Recent research underscores the viability of this approach, demonstrating a strong correlation between the assessments of two distinct large language models – gpt-oss-120b and Qwen3-235B – when evaluating application permission risks. This consistency suggests a robust and reliable method for identifying potentially dangerous permission configurations. By proactively scrutinizing and limiting application access to only the necessary resources, organizations can significantly reduce their vulnerability to privilege escalation attacks and unauthorized data breaches, fostering a more resilient security framework.

The pursuit of robust application security, as detailed in the research concerning malicious Entra OAuth apps, inherently acknowledges the ephemeral nature of defenses. Any improvement in permission risk scoring, or consent pattern anomaly detection, ages faster than expected, requiring continuous adaptation. This aligns with Marvin Minsky’s observation: “The most effective way to learn is to begin with a problem, not a solution.” The framework detailed here doesn’t present a final solution, but rather a dynamic system for identifying and mitigating risks – a testament to the necessity of perpetually refining security measures against evolving threats. The inherent challenge lies not in achieving perfect security, but in managing its inevitable decay with graceful, iterative improvement.

What Lies Ahead?

The presented framework, while a necessary intervention against the inevitable entropy of access management, merely addresses a symptom. The proliferation of OAuth applications, each a potential vector for compromise, is less a technical problem and more a reflection of systemic complexity. Permission risk scoring, even when LLM-assisted, is a transient bulwark against the persistent tide of novel attack surfaces. Each identified anomalous consent pattern is a temporary equilibrium, quickly disrupted by evolving malicious tactics.

Future work must move beyond reactive detection. The true challenge lies in anticipating the shape of abuse, not just identifying its echoes. Research should explore methods for proactively modeling legitimate application behavior, establishing a baseline against which deviations can be measured with greater precision. Furthermore, a deeper understanding of user consent fatigue-the willingness to grant broad permissions without careful consideration-is crucial. Treating consent as a static act ignores the inherent decay of user attention.

Ultimately, the pursuit of perfect security is a fool’s errand. Infrastructure, like all systems, is destined for eventual failure. The goal, then, is not to prevent compromise, but to build systems that age gracefully, minimizing the blast radius and accelerating recovery when-not if-compromise occurs. Technical debt, in this context, is akin to erosion; continuous monitoring and proactive remediation are the only viable long-term strategies.

Original article: https://arxiv.org/pdf/2512.15781.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-21 18:43