Kill BOLAs Before They Escape: Prevent API Authorization Vulnerabilities

Broken Object Level Authorization (BOLA) and Broken Object Property Level Authorization (BOPLA) are among the most critical security threats to modern APIs. These vulnerabilities continue to be a top concern due to their subtlety, prevalence, and potential for unauthorized access or privilege escalation. In this paper, we examine the structural causes of BOLA and BOPLA, why traditional tools fail to detect them, and propose a scalable AI-driven solution for early detection and remediation. Our system, based on Aptori’s semantic analysis platform, continuously analyzes API behavior and authorization logic in code, identifying vulnerabilities prior to deployment. We describe the detection methodology, implementation model, and its ability to proactively mitigate BOLA/BOPLA issues in enterprise environments.  

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1. INTRODUCTION

Modern APIs are foundational to digital transformation, connecting microservices, mobile apps, and external ecosystems. However, with the growth of API-first development, security vulnerabilities like BOLA and BOPLA have emerged as the most critical threats. Both attack classes are rooted in insufficient authorization checks, leading to unauthorized data access or privilege escalation. Traditional testing and perimeter defenses fall short in identifying these logic flaws. This paper presents an approach based on semantic code analysis and runtime behavior modeling, implemented in Aptori, that detects and prevents these flaws before deployment.

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2. MOTIVATION AND BACKGROUND

API security has emerged as a paramount concern for enterprises, driven by the escalating frequency of attacks seeking to exploit vulnerabilities within APIs. Surveys conducted in early 2023 indicate that 78% to 92% of organizations experienced security incidents related to APIs in the previous year. Among the OWASP API Security Top 10, Broken Object Level Authorization (BOLA) and Broken Object Property Level Authorization (BOPLA) emerge as the most prevalent and high-risk weaknesses.

This raises a pertinent question: In the face of a plethora of security tools deployed during development and runtime—such as SAST, SCA, DAST, API gateways, and web application firewalls (WAF)—why do API security breaches persist? And more importantly, what are the functional and architectural requirements for an effective BOLA killer?  

2.1 Why Existing Tools Fall Short

Despite the widespread use of static analysis (SAST), dynamic scanning (DAST), software composition analysis (SCA), API gateways, and web application firewalls (WAFs), API-related breaches remain prevalent. These tools, while valuable in detecting known vulnerabilities and misconfigurations, operate with limited awareness of the business logic that governs API behavior. They lack the contextual understanding necessary to enforce object-level access control, which is the foundation of preventing BOLA and BOPLA.

Crucially, authorization decisions are not universal—they are implementation-specific and tightly coupled with the application’s domain model, user roles, and resource relationships. As a result, identifying BOLA or BOPLA issues requires not just scanning code, but interpreting how authorization is applied to individual resource instances and fields based on that context.

This gap exposes a fundamental limitation in current security tooling: they are not equipped to reason about who can access what and why, based on the semantics of the API and its intended policy. Addressing this limitation requires a new approach—one that treats authorization as code and tests it accordingly.

2.2 Authentication ≠Authorization

It is common—yet dangerous—to assume that strong authentication alone can prevent unauthorized access to sensitive data or functionality. While authentication verifies who the user is, authorization determines what that user is allowed to do.

Authentication mechanisms such as OAuth tokens, JWTs, and multi-factor login are necessary components of modern API security. However, they act only as the front gate. Once inside, it is the authorization logic that enforces the boundaries of access between users, objects, and actions.

Consider the analogy of a secure building: authentication is the badge that gets you through the front door; authorization is the system that decides which rooms you can enter. If that system is flawed—say, any badge opens every door—then no matter how secure the badge is, intruders can still move laterally within the system.

This distinction is central to understanding why BOLA and BOPLA are so dangerous. These vulnerabilities do not result from weak authentication, but from incorrect or missing checks in the logic that governs object-level access. In this way, they often go undetected by authentication-centric tools and surface only during runtime under specific conditions.

Solving this problem requires treating authorization logic as a testable, verifiable component of the software, not an afterthought delegated to access-control libraries or perimeter policies.

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3. AUTHORIZATION LOGIC: CHALLENGES AND COMPLEXITY

Effective API security hinges on the correctness of authorization logic. While authentication mechanisms validate user identity, authorization ensures users can only perform permitted actions on resources they are entitled to access. This section explores how authorization policies manifest as business logic, the limitations of traditional tooling in detecting flaws, and the inherent complexity of validating these policies through exhaustive testing.

3.1 Authorization as Business Logic

Authorization policies can range from simple role-based access control (RBAC) to complex attribute-based access control (ABAC), contextual rules, or domain-specific conditions. As the sophistication of the policy increases, so does the complexity of the application logic responsible for enforcing it. This logic is often deeply embedded within the codebase, spread across multiple microservices or API layers.

Unlike configuration-based security controls, authorization rules are application-specific and dynamic. For example, the logic that dictates whether a user can read an event or update a resource depends not only on the user’s role but also on relationships such as ownership, organizational context, or even time-bound conditions.

This makes authorization enforcement part of the business logic, which is difficult to analyze using conventional static or dynamic tools. Vulnerabilities such as BOLA (Broken Object Level Authorization) and BOPLA (Broken Object Property Level Authorization) are direct results of inconsistencies or omissions in this logic. These vulnerabilities are not structural flaws but semantic errors—errors in meaning, not syntax.

3.2 Why Authorization Testing is Hard

To verify authorization logic, software teams must simulate a diverse range of real-world conditions. This includes varying:

• Actors (e.g., owners, peers, admins)
• Actions (e.g., read, update, delete)
• Resource types and instances (e.g., events, users)
• Contextual parameters (e.g., organization, tenant ID)

Even a simple policy can require significant test coverage. Consider the following example:

Policy: A user who creates an event can read, update, and delete it. An admin user can read any event.

There are two possible types of creators (regular and admin users), three types of actors (creator, another user, admin), and three possible actions. The total number of test cases becomes:

2 creators × 3 actors × 3 actions = 18 test cases

Now, introduce multi-tenancy, where users and admins are scoped to organizations, and cross-org access is forbidden:

Policy: Admins can only access events within their own organization.

This policy introduces:

4 creators (user/admin from Org1 and Org2)

5 actors (creator, peer user/admin in same org, user/admin in different orgs)

3 actions

Now the total is:

4 × 5 × 3 = 60 test cases

This is a 300% increase in complexity from one simple policy refinement.

3.4 Manual Testing is Unsustainable

Real applications often include dozens of resource types, each with their own authorization rules. As logic grows more fine-grained and applications support multiple roles, entities, and multi-tenancy, the volume of test cases grows combinatorially.

Manual test creation and maintenance becomes:

Tedious: Requires enumerating all permutations of actors, actions, and resources.

Error-prone: Developers may overlook corner cases or fail to update tests as policies evolve.

Non-continuous: Tests are often run infrequently or only in later stages of development.

These constraints severely limit the ability of security and engineering teams to verify that access control logic is functioning as intended.

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4. THE BOLA KILLER: AUTONOMOUS TESTING OF AUTHORIZATION POLICIES 

Aptori’s Sift is designed to address the complexity and volume of test cases required for verifying API authorization policies. This section describes how Sift generates, executes, and interprets tests based on a formal definition of access rules, transforming authorization logic into a continuously verifiable component of the software.

4.1 Declarative Authorization Policies

At the heart of Sift’s approach is the ability to represent access control policies in a declarative format. These policies specify:

• The resource types (e.g., event, user)
• The owner of each resource
• The roles and identities of actors
• The permitted actions (e.g., read, update, delete)

This policy model abstracts the logic from the application code, enabling consistent and reusable validation across services.

Example Use Case: Calendly-like API

Consider an API for a scheduling platform. It exposes a RESTful endpoint to read an event

GET /api/v1/events/{event_id}

The access policy requires:

• Only the creator of the event can update or delete it.
• Admins can read any event within their own organization.
• Users from other organizations must be denied access entirely.

Imagine the product owner providing the simple authorization policy described above as a table of permissions by role.

Table 1. Table of permissions by Role

This seeming simple policy requires 60 test cases to fully validate the authorization policy.  Given the authorization policy and the API definition, Sift autonomously generates and executes all 60 test cases.

The authorization policy can be represented in Sift's YAML configuration as follows.

policy:
  - resources:
      - event
    owner: userOrg1
    permissions:
      - user: adminOrg1
        access: ["read"]
      - user: userOrg1
        access: ["read", "update", "delete"]
  - resources:
      - event
    owner: userOrg2
    permissions:
      - user: adminOrg2
        access: ["read"]
      - user: userOrg2
        access: ["read", "update", "delete"]

Figure 1: Example YAML definition: This simple policy already represents multiple dimensions—ownership, role, organizational boundary, and permitted operations.

This single YAML block implicitly defines access control across:

• Multiple roles (regular vs admin)
• Multiple organizations (Org1 vs Org2)
• Multiple actions (read, update, delete)
• Ownership relationships 

Sift interprets this policy to generate all required test cases for each user/resource/action combination—automatically.

4.2 Automated Test Case Generation

Using the declarative policy, Sift automatically generates all combinatorial permutations of actors, resources, and actions to form valid and invalid test scenarios. This ensures full coverage of:

• Expected access (positive tests)
• Prohibited access (negative tests)
• Boundary and cross-tenant conditions

In the example, Sift autonomously creates all 60 test cases across four creators, five actors, and three actions. The platform supports generating thousands of such cases in seconds, removing the bottleneck of manual test design.

4.3 Execution and Enforcement Validation

Once generated, the test cases are executed against the actual API environment—either in staging or pre-production. Each test request is evaluated for:

• Correct authorization enforcement (expected responses)
• Misconfigurations or logic gaps (unexpected access granted)
• Absence of checks (missing 403/401 conditions)

Sift supports replay-based validation as well as real-time inspection of HTTP status codes and API responses, providing fast and actionable insights. leave 3.81 cm (1.5") of blank text box at the bottom of the left column of the first page for the copyright notice.

4.4 Continuous Integration and Feedback

Sift is designed to operate continuously as part of the CI/CD pipeline. It integrates directly with version control systems and CI runners to:

• Trigger tests on every code change or pull request
• Fail builds when authorization regressions are detected
• Provide remediation guidance or code suggestions

This shift-left and shift-right model enables security teams to enforce policies early and often—while giving developers feedback within their natural workflow.

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5. CONCLUSION

BOLA and BOPLA represent the most dangerous yet often undetected threats in API security. Perimeter defenses and conventional scans cannot understand authorization logic. This paper presents a solution that applies semantic analysis and contextual reasoning to automatically detect and prevent these vulnerabilities. As APIs continue to dominate digital ecosystems, such proactive and scalable defenses are critical to resilient software architecture.

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