OWASP NHI5:2025 - Overprivileged NHI In-Depth Analysis and Management

Analyzing the Latent Risk Factor Within Systems, 'Excessive Permissions'

In your company's digital infrastructure operations, the role of Non-Human Identities (NHIs) – including service accounts, API keys, IoT devices, and now AI agents – is increasingly critical. While they bring innovation and efficiency, they can also harbor serious security risks, notably 'excessive permissions'.

Furthermore, recent AI technologies like A2A (AI-to-AI) interactions and MCP (Machine Conversation Platforms) increase the autonomy and unpredictability of NHIs, further amplifying these risks. The OWASP NHI Top 10 list provides a useful benchmark for identifying key risks to focus on in this complex environment.

This article aims to provide an in-depth guide to the sixth item, NHI5: Excessive Permissions, analyzing its causes and impacts, and presenting practical management strategies. This is more than just a configuration error; it's a fundamental security issue that can significantly impact business operations. As we enter the AI era, managing this issue has become even more critical.

The Severity of NHI5: Why Are Excessive Permissions a Significant Threat? (Amplified Risks in the AI Era)

When an NHI is compromised, the scope and severity of the damage are directly proportional to the permissions it holds. The compromise of an NHI with only the minimum necessary permissions might have a relatively limited impact. However, if an NHI with broad (excessive) permissions is compromised, attackers can leverage these permissions to access systems broadly, exfiltrate sensitive data, and cause severe consequences. In essence, excessive permissions act as a 'Blast Radius Multiplier', greatly amplifying the impact of an initial breach.

Particularly concerning is the scenario where NHIs in A2A/MCP environments, which learn and interact with other AIs autonomously, possess excessive permissions. The risk becomes more unpredictable and widespread. For example, an AI agent making an incorrect judgment could use its excessive permissions to propagate inaccurate information to other AI agents or trigger cascading malfunctions. Thus, excessive permissions, often hidden within the complexity of cloud environments, represent a latent risk factor that gradually weakens an organization's security posture. Let's now delve into why this high-risk 'excessive permissions' issue arises and how it can be effectively managed.

NHI5 In-Depth Analysis: How Does the 'Excessive Permissions' Issue Arise and Intensify?

The problem of excessive permissions often stems more from organizational management practices, policy development, and operational habits than from specific technical flaws. The introduction of AI technology adds new layers of complexity.

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1. "Grant Broadly First" – Prioritizing Convenience and Development Speed:
   
   • This is one of the most common causes. Accurately analyzing and assigning the minimum necessary permissions for each NHI requires time and effort. Instead, the approach of "grant broad permissions first and adjust later" or simply assigning default cloud provider administrator roles can seem faster and more convenient in the short term. However, this practice becomes a major source of increased security risk in the long run.
2. "Unclear Permission Scope" – Difficulties Due to Complexity and Lack of Understanding:
   
   • In microservices architectures or complex cloud service environments, perfectly identifying the exact API call permissions or data access rights needed for a specific NHI task can be challenging. This is especially true for AI agents that learn and whose required functions change dynamically, making it extremely difficult to accurately predict and define all necessary permissions in advance. Consequently, permissions broader than actually needed are often granted.
3. "Create and Forget" – Flaws in Role Design and Lack of Lifecycle Management:
   
   • Even when using Role-Based Access Control (RBAC), issues arise if roles are overly granular, making management complex (Role Explosion), or conversely, if a single role encompasses too many permissions (Overly Broad Roles), violating the principle of least privilege. Furthermore, the phenomenon of 'Permission Creep' occurs when new permissions are continuously added as an NHI's role changes, but obsolete permissions are not revoked due to inadequate or manual review processes. This increases latent risks by accumulating unmanaged 'idle permissions'.
4. "Using Defaults" – Overlooking the Risks of Default Settings:
   
   • Some cloud services, SaaS applications, and AI development platforms may be configured with relatively broad permissions by default for initial ease of use. If users do not carefully review and adjust these default settings to the necessary level, they might operate systems with unintentionally granted excessive permissions.
5. "Copy-Pasting Code" – IaC Configuration Errors and Lack of Verification:
   
   • While managing infrastructure and permissions efficiently through Infrastructure as Code (IaC) tools like Terraform and CloudFormation is recommended, it also carries risks. Using unverified code snippets from the internet, insufficiently reviewing permission settings during code writing, or relying solely on terraform plan outputs without thoroughly verifying the actual permission impact can lead to excessive permissions being codified and deployed into the system.
6. "Using Group Membership" – Unintended Permission Inheritance Through Groups:
   
   • Including an NHI in a specific user or service account group can lead to it inheriting unintended excessive permissions (e.g., access rights to other departments' systems) due to policies or roles attached to that group. Complex nested group structures make tracking and managing these inherited permissions even more difficult.
7. "Ensuring Smooth AI Communication" – Proactive Over-Provisioning in A2A/MCP Environments:
   
   • To facilitate seamless information exchange and collaboration between AI agents, there might be a tendency during the development phase to grant very broad permissions preemptively, covering all conceivable interaction scenarios. This can stem from the practical difficulty of strictly defining and managing least privilege amidst dynamic and unpredictable interactions.

Detecting and Measuring Excessive Permissions: Identifying Latent Risks Within Systems

Since excessive permissions are not easily visible, a systematic approach to effectively detect and quantitatively assess them is crucial. The emergence of AI-based NHIs presents new detection challenges.

1. Limitations of Manual Audits: As previously mentioned, manual audits are inefficient in terms of time and cost and prone to errors in modern IT environments with vast numbers of NHIs and complex permission policies, making completeness and accuracy difficult to guarantee.
2. Active Utilization of Cloud-Native Tools: Cloud providers offer powerful tools to help address this issue.
   
   • AWS Access Analyzer: Beyond analyzing external accessibility, it identifies Unused Permissions over a specified period, validates policy syntax, and even helps generate least-privilege policy drafts.
   • Azure Permissions Management (formerly CloudKnox): Analyzes permissions across multi-cloud environments, quantifies risk using a Permission Creep Index (PCI), and provides specific Right-sizing recommendations based on usage.
   • GCP IAM Recommender: Uses machine learning to identify permissions in assigned roles for service accounts, etc., that are deemed excessive compared to actual usage patterns and recommends more appropriate predefined or custom roles.
3. Adoption of CIEM Solutions (Specialized Permission Management): Cloud Infrastructure Entitlement Management (CIEM) solutions are key tools for resolving excessive permission issues. They support systematic management through features like:
   
   • Comprehensive Visibility: Provides a unified view of all NHIs, their assigned permissions, and actual usage across multi-cloud and hybrid environments.
   • Automated Risk Detection: Continuously and automatically detects and alerts on least privilege violations, excessive/unused permissions, privilege escalation paths, and toxic combinations of permissions.
   • AI-Based Analysis Support: Can be utilized to analyze the complex and dynamic permission usage patterns of AI-based NHIs, capture anomalies, and predict necessary permission scopes.
   • Permission Optimization and JIT Management Support: Suggests permission reduction measures based on detected risks and manages Just-In-Time (JIT) access request/approval/revocation workflows.
4. Usage Log Analysis for Permission Optimization (Data-Driven Approach):
   
   • Systematically analyze activity logs (e.g., CloudTrail, Azure Monitor Logs, Google Cloud Audit Logs) to accurately determine which permissions each NHI has actually used over a specific period (e.g., 90, 180 days). Compare this usage data with granted permissions to identify long-unused permissions. Reassess the necessity of these permissions and remove them ('Right-sizing'). For AI agents, a more sophisticated approach is needed to distinguish between permission usage during normal learning/exploration phases and permissions required for actual operational tasks, necessitating continuous log analysis and dynamic adjustments.
5. Quantitative Risk Assessment and Management Prioritization:
   
   • Not all excessive permissions pose the same level of risk. Evaluate the risk level of each NHI or permission assignment by comprehensively considering factors like the sensitivity of accessible data, the impact of performable actions (e.g., delete/modify vs. read), and (for AI) the importance and trustworthiness of interacting systems/AIs. This allows organizations to prioritize addressing the riskiest excessive permissions first with limited resources.
6. AI Behavior Modeling and Anomaly Detection (Future Direction):
   
   • The advancement of approaches using machine learning to model the normal behavior and permission usage patterns of AI-based NHIs is expected. Detecting deviations from these models as potential threats (compromise or malfunction) can complement traditional static rule-based detection.

Impact Analysis: Major Risk Scenarios Caused by Excessive Permissions

Excessive permissions exacerbate the damage during security incidents, provide attackers with advantageous conditions, and hinder defense efforts. In AI environments, these impacts can manifest in more complex ways.

• Spread Across Internal Systems (Lateral Movement & Domain Dominance): If a compromised NHI possesses broad control permissions like Active Directory modification rights, hypervisor access, or cloud IAM administrative privileges, attackers can use this to easily move to other critical systems on the internal network and potentially achieve full domain dominance.
• Provision of Unexpected Privilege Escalation Paths (Privilege Escalation Chains): Even permissions that seem non-critical individually can form dangerous privilege escalation chains when combined (e.g., permission to write to a specific configuration file + permission to restart a service + permission to execute certain commands). CIEM tools can help identify such risky permission combinations.
• Increased Risk of Large-Scale Data Exfiltration & Destruction: Excessive access rights to storage services (S3, Azure Blob, etc.) can be a direct cause of large-scale data breaches. Furthermore, permissions like database administration or storage volume deletion can lead to the permanent destruction of critical data.
• Potential for Infrastructure Disruption & Financial Damage: If an NHI compromised has permissions to create/delete compute resources (EC2, VMs), modify network configurations (VPCs, firewalls), or change DNS settings, attackers could disrupt entire infrastructure operations or secretly generate expensive resources, causing significant financial losses (e.g., cryptojacking).
• Facilitation of Defense Evasion & Persistence: Attackers can use the excessive permissions of a compromised NHI (e.g., rights to modify security tool configurations, manage audit logs) to disable security systems, erase their tracks, and remain undetected within the system for extended periods. They might also create new administrative-level NHIs to secure persistent access (backdoors).
• Cascading AI Risks & Trust Erosion: If an AI agent with excessive permissions is compromised, attackers could manipulate it to issue malicious commands or inject manipulated data into other interacting AI systems. This could lead to unpredictable cascading system failures, flawed business predictions, exfiltration or contamination of sensitive training data, and other complex, hard-to-recover consequences, ultimately severely eroding trust in the entire AI system.

Advanced Defense Strategies: Practical Measures for Addressing Excessive Permissions (Including AI Era)

Effectively resolving the excessive permissions issue requires a multi-faceted approach combining technical controls and systematic management processes.

1. Strategic Utilization of CIEM Solutions (Centralizing Permission Management):
   
   • CIEM solutions should be leveraged not just as detection tools but as the 'central control center' for enterprise-wide NHI permission management. Actively utilize all features – continuous visibility, automated risk analysis and prioritization, actionable optimization recommendations, JIT access workflow integration, compliance report generation – to build a data-driven, intelligent permission management system. This role is crucial for continuously tracking and adaptively managing the dynamic and complex permission requirements of AI-based NHIs.
2. Proactive Prevention via Policy as Code (PaC) ('Shift Left'):
   
   • Integrate policy engines like Open Policy Agent (OPA) and related tools (e.g., conftest) into CI/CD pipelines to automatically verify IaC code (Terraform, CloudFormation, Kubernetes YAML, etc.) against predefined minimum privilege security policies before deployment. Examples include rules like "prohibit assignment of administrator roles (*:*) to any resource (*)", or "require specific sensitive actions to always be used with conditions". By failing builds or deployments that violate these policies, you can effectively prevent risky permission configurations from reaching production environments. Apply the same principles consistently to AI model deployment and related infrastructure configuration pipelines.
3. Implementation of Granular and Dynamic Access Control (Aiming for Zero Standing Privilege):
   
   • When writing IAM policies, specify resource identifiers (ARNs, IDs, etc.) as granularly as possible. Explicitly Allow only the minimum necessary actions, denying everything else by default. Actively utilize Attribute-Based Access Control (ABAC) to make access decisions dynamically based on a combination of attributes such as the NHI's role, properties (e.g., project tags), the sensitivity classification of target data, request time, IP address, and (for AI) the context of the current task or the trust score of an interacting entity. Make full use of cloud providers' condition operators to refine the scope of policy application precisely. The ultimate goal should be 'Zero Standing Privilege', a model where NHIs have minimal or no standing permissions and acquire necessary permissions only when needed for a specific task.
4. Strict Application of Separation of Duties Principles to NHIs:
   
   • Just as critical human tasks are segregated, apply the principle of separation of duties to NHI permissions. For example, separate the permission to change a database schema from the permission to back up/restore data, or separate the permission to build code from the permission to deploy to production. In A2A environments, design critical decisions or system changes to require consensus or independent verification from multiple AI agents to distribute risk and prevent single points of failure or abuse.
5. Operation of a Substantive 'Responsibility-Based Attestation' Process:
   
   • Implement a formal, periodic permission review and attestation process where the NHI owner (or the owner of the service/model using the NHI) does more than just check a box. They must review the assigned permissions, provide clear justification for why each permission is still necessary, and take responsibility for the outcome. Automate and track this process using CIEM tools or ITSM system integration. Implement workflows to automatically remove permissions that are not attested or deemed unnecessary according to defined procedures.
6. Full Adoption and Intelligent Advancement of JIT Access:
   

1. 
   
   • Aim to adopt Just-In-Time (JIT) access not just for exceptional high-risk tasks but as the standard model for granting NHI permissions whenever feasible. Build systems where NHIs dynamically receive the minimum necessary permissions, only for a strictly limited duration (e.g., estimated task time + buffer), through a predefined and approved workflow (e.g., automated request/approval system), with permissions automatically revoked upon task completion or timeout. For AI agents, this needs to evolve towards intelligent JIT mechanisms that can dynamically predict required permissions based on anticipated tasks or real-time anomaly detection, or preemptively reduce/revoke permissions upon detecting risks. This is one of the most effective strategies to fundamentally reduce the risks associated with standing excessive privileges.
2. AI for Security: Managing NHI Permission Risks with AI Technology:
   
   • While AI introduces complexity, it also offers solutions. Leverage machine learning-based User and Entity Behavior Analytics (UEBA) techniques to learn the normal permission usage patterns of NHIs (especially AI-based ones) and detect anomalous behavior in real-time to identify potential security threats early. Furthermore, actively explore and consider adopting intelligent permission management automation capabilities where AI continuously analyzes permission usage and policy configurations to automatically recommend optimal least-privilege policies or flag high-risk permission change requests for priority review by security teams.

Future Outlook: An Era Where Management is Impossible Without Automation and AI

The proliferation of cloud-native architectures, multi-cloud, and hybrid environments will continue, and the adoption of A2A interactions and autonomous AI agents will exponentially increase the complexity of NHI permission management. In such environments, effective management through manual processes and periodic audits alone is no longer feasible. The problem of excessive permissions will likely become more severe and harder to detect.

Therefore, the adoption of advanced automation technologies such as CIEM, Policy as Code, and JIT access will become essential, not optional. Ultimately, a paradigm shift towards leveraging AI technology itself to intelligently, dynamically, and continuously manage and control the permissions of the burgeoning population of AI-based NHIs will be necessary. The era where AI monitors and controls the behavior and permissions of other AIs is becoming a reality.

Conclusion: Excessive Permissions, a Core Organizational Risk That Cannot Be Ignored

NHI5: Excessive Permissions is not merely a technical configuration error but a fundamental risk factor that seriously threatens core business assets and continuity. The advancement and convergence of AI technology further amplify this risk, increasing the complexity and importance of its management.

To effectively manage this potential risk and realize Zero Trust security principles, organizations must immediately strengthen their efforts in the following areas:

• Establish the Principle of Least Privilege (PoLP) not just as a slogan but as a fundamental principle and culture applied to all IT activities, consistently enforced through technical means.
• Actively adopt automated solutions like CIEM and Policy as Code to continuously detect, prevent, and optimize excessive permissions 24/7.
• Adopt JIT access as a standard permission management model and evolve it into more intelligent and dynamic forms suitable for the AI environment.
• Operate a substantive, responsibility-based permission review and attestation process regularly.
• Implement granular and dynamic access control using ABAC, conditional policies, etc., to achieve fine-grained permission management tailored to context.
• Continuously explore and prepare for the adoption of next-generation intelligent permission management and threat detection systems leveraging AI technology.

Addressing the problem of excessive permissions is a long-term commitment requiring continuous attention and effort. Organizations must recognize NHI permission management as a core security management domain and safeguard their valuable systems and data from ever-evolving threats through automated technologies, robust processes, and innovative approaches prepared for the AI era.

For deeper insights and specific solutions regarding NHI security and permission management, please feel free to contact Cremit. Our experts are ready to provide full support. Also, you can find continuously updated relevant information on the Cremit Blog and compliance-related resources.