Part 10 of our OWASP API Security Top 10 Deep Dive Series
The Object Heist: How API1:2023 Turned Digital IDs Into Master Keys
The Great ID Switcheroo
You walk into a hotel, flash your room key, and head toward the elevator. But instead of going to room 237, you try 238. Then 239. To your amazement, your key works on every door. You now have access to every guest’s belongings and personal space, not because you’re a master thief, but because the hotel forgot to check if your key should open those rooms.
This isn’t a privacy-thriller nightmare. It’s the digital reality that API attacks exploit daily, making it the undisputed champion of the OWASP API Security Top 10. Welcome to Broken Object Level Authorization (BOLA), the most dangerous API security risk since the list’s inception in 2019.
BOLA is a failure in digital identity verification, not the “who are you?” that authentication handles, but the “are you allowed to access this specific thing?” that trips up even experienced developers. When a researcher found that Uber’s API let any authenticated user control any other account by simply changing a user ID in the request, they revealed a perfect example of how BOLA turns routine API calls into universal access codes.
The Anatomy of Digital Identity Confusion
To understand why BOLA is the apex predator of API vulnerabilities, we need to examine the architecture of modern applications. Object Level Authorization works like a sophisticated bouncer – not just checking IDs at the door (authentication), but verifying each person’s right to access specific VIP areas, private booths, or backstage passes (authorization at the object level).
OWASP defines BOLA as occurring when APIs fail to verify whether an authenticated user has permission to access specific resources. This happens when applications have strong authentication but forget to check if User A should actually access User B’s data, documents, transactions, or account information.
The attack is usually deceptively simple: Alice logs in, gets valid tokens, and sees her profile via GET /api/users/12345/profile. She changes it to GET /api/users/12346/profile – and if Bob’s profile appears, BOLA has struck.
Microservices often amplify this risk. One service might validate Alice’s session, while another accepts her token and assumes it’s valid for any user ID. This separation, while great for scalability, creates perfect conditions for BOLA exploitation.
Case Studies in Digital Identity Theft
Real-world BOLA attacks show how this vulnerability translates into devastating consequences. The incidents we know about are just the tip of the iceberg – most go undetected for months or years, hiding as legitimate user activity.
The Uber Account Takeover (2019) is a prime example. In 2019, researcher Anand Prakash showed how chaining two Uber API weaknesses, one leaking a user’s UUID, another leaking user info and a mobile auth token, enabled complete account takeover. A perfect illustration of object/identifier misuse leading to unauthorized access. Attackers could track victims’ locations, book rides, and access Uber Driver or Uber Eats accounts. The elegance lay in its simplicity; every step used legitimate functionality, making detection by traditional tools nearly impossible.
The Parler Data Extraction (2021), ≈70 TB, stemmed from no auth + sequential IDs + weak rate limiting, enabling mass enumeration. It’s a close cousin to BOLA (insufficient authorization/IDOR), and shows how returning objects without proper checks leads to bulk exposure. Attackers found that API endpoints used sequential post IDs without authorization checks, enabling the automated extraction of data, posts, images, videos, and user details. With no rate limiting and no verification of user permissions, attackers simply iterated through post IDs in sequence, retrieving complete content with metadata, geolocation, and personal details. Within days, they had the platform’s entire dataset, showing BOLA’s potential for intelligence gathering at unprecedented scale.
T‑Mobile (2018, 2023) experienced significant breaches tied to unauthorized API access (2.3M and 37M customers, respectively). They underscore how weak object/authorization controls and monitoring around APIs can drive mega‑scale exposure, even when auth exists.
These breaches highlight a pattern where APIs, built to handle massive customer bases, often prioritize performance over granular authorization checks. Business logic assumes authenticated customer service reps or automated systems should have broad data access, but fails to implement sufficient controls when those patterns are exploited by unauthorized parties.
Authorization Blindness
Authorization blindness happens when teams focus heavily on authentication, the cryptographic protocols, token systems, and identity checks, while treating object-level authorization as secondary.
Research shows developers are more likely to implement strong authentication than granular authorization. This stems from completion bias; solving the authentication puzzle gives a false sense that authorization will follow.
The complexity worsens things: as apps adopt microservices, containers, and cloud-native designs, authorization decision points multiply. Each service, endpoint, and data object becomes a potential gap, but the human mind struggles to track such interconnected rules.
Mental model misalignment adds to the problem. Developers often think in RBAC terms, admins do admin tasks, regular users do regular tasks, but object-level authorization needs context: this user, this object, this situation, this purpose. This doesn’t fit neatly into RBAC frameworks.
Finally, the automation paradox: as DevOps pipelines and CI/CD grow, testing often checks functionality, not security. Automated tests may confirm that /api/users/{userID}/profile works for authenticated users, but rarely verify if User A can’t access User B’s profile by changing the userID.
The Technical Machinery of Object Manipulation
BOLA attacks exploit the architecture of RESTful APIs, where resource identification often follows predictable patterns. Understanding them means examining how applications handle object references and state management.
Sequential ID Enumeration is the most basic vector. When applications use auto-incrementing IDs (1, 2, 3…), attackers can iterate through them to access unauthorized resources by first establishing a valid session, finding endpoints exposing IDs, and then looping through ID ranges.
GUID/UUID manipulation shows that even complex identifiers can be vulnerable. IDs are often harvested from responses, logs, or error messages and then replayed across endpoints.
Hierarchical Object Exploitation targets nested resources such as /api/companies/{companyID}/departments/{departmentID}/employees/{employeeID}. Even if each ID is authorized individually, combinations may create gaps, allowing access to other companies’ data.
Parameter Pollution exploits APIs that accept IDs via multiple channels – URL paths, query parameters, headers, and bodies. Conflicting IDs in different parameters can trick validation logic, e.g., userID=123 in the path but userID=456 in the body.
Business Logic Exploitation leverages legitimate workflows, exposing object references. For example, a password reset flow might reveal security questions for any account, enabling ID iteration to harvest sensitive data.
GraphQL-specific BOLA exploits complex queries that traverse object relationships. Under-protected resolvers may return related objects without independent authorization checks, like retrieving a user’s profile plus nested projects, documents, or team info.
Building Authorization Intelligence
Preventing BOLA vulnerabilities requires shifting from perimeter-based security to contextual authorization intelligence. The goal is not just authenticating users, but continuously validating their relationship to specific resources during every interaction.
Implement Explicit Authorization Checks at every API endpoint handling object identifiers. Go beyond simple authentication token validation to context-aware decisions. Every request should answer: Who is making this request? What resource are they trying to access? Do they have permission for this specific action on this specific resource?
Code-level implementation requires establishing authorization logic as a first-class citizen in application architecture. Consider this Python example using a decorator pattern:
@authorize_object_access(‘user’, ‘read’)
def get_user_profile(current_user, user_id):
# Authorization decorator validates that current_user
# can read user object with user_id
return User.objects.get(id=user_id)
Contextual Authorization Frameworks provide systematic approaches to object-level authorization. Attribute-Based Access Control (ABAC) enables fine-grained decisions based on user attributes, resource attributes, and environmental conditions. Relationship-Based Access Control (ReBAC) models explicit relationships between users and objects, ensuring authorization reflects real-world ownership and permissions.
Use Unpredictable Object Identifiers to make BOLA attacks harder. While this is defense in depth, random UUIDs instead of sequential IDs force attackers to discover identifiers through other means. Consider cryptographically random IDs like 7f8b2f5e-3d1c-4a9e-8f7b-1e2d3c4b5a6f over simple numbers.
Implement Comprehensive Logging and Monitoring to detect BOLA attacks in progress. Authorization failures, unusual access patterns, and systematic enumeration attempts often signal exploitation. SIEM systems should watch for:
- Rapid requests to similar endpoints with different object IDs
- Access attempts outside a user’s normal scope
- Tokens are used to access resources created by others
- Unusual geographic or time-based access patterns
API Gateway Authorization offers centralized control over object-level decisions. Modern gateways can enforce policies that understand object ownership and user relationships, but must coordinate with application-level business logic for consistency.
The Future of Object-Level Authorization
As API architectures grow in complexity and scale, BOLA prevention must adapt. The future lies in intelligent, context-aware systems that understand both technical relationships and business logic.
The choice is clear: build smart authorization that understands nuanced user-object relationships, or become a cautionary tale. In an API-driven world, every object identifier can be a master key, and only intelligent locks can keep them safe.
Frequently Asked Questions About Broken Object Level Authorization (BOLA)
Q: What’s the difference between authentication and authorization in BOLA?
A: Authentication checks “who are you?” (like showing ID at a nightclub). Authorization checks: “are you allowed here?” (VIP booth access). BOLA happens when APIs verify identity but skip object-level checks, letting a user access other people’s data by changing an ID in the request.
Q: Why is BOLA the #1 API security risk?
A: It’s common, easy to exploit (just change a number in a URL), and devastating (mass data theft or account takeover). Attacks look like normal traffic, making them hard to detect.
Q: Example of a BOLA attack?
A: Logged into an app, you see your account at /api/accounts/12345. Change it to /api/accounts/12346 – if you see someone else’s info, that’s BOLA.
Q: How is BOLA different from BFLA?
A: BOLA is “wrong object, right function” (accessing another user’s data). BFLA is “wrong function” (regular user accessing admin-only features).
Q: Why don’t firewalls and gateways catch BOLA?
A: Requests look valid: /api/users/12345 vs /api/users/12346 are both authenticated and well-formed. Tools can’t see the business logic that says user 12345 shouldn’t see 12346’s data.
Q: Best way to test for BOLA?
A: Use multiple test accounts and try changing object IDs to access each other’s resources. Tools like APIsec or Burp help, but manual testing is usually required to catch business logic flaws.
Q: Which apps are most vulnerable?
A: Multi-tenant SaaS, social media, e-commerce, and financial apps, especially those with predictable IDs. But any app with user-specific objects can be at risk.
Q: How serious is the business impact?
A: The average breach costs $4.88M, plus regulatory penalties and brand damage.
Q: Difference between BOLA and IDOR?
A: IDOR is the older term; in modern APIs, the same class is framed as BOLA..
Q: Impact of microservices/cloud?
A: They increase risk by adding more authorization points, but can reduce it with centralized authorization services. Every service must validate object-level permissions.
Q: Role of business logic?
A: Critical BOLA prevention requires understanding user–object relationships and workflows, not just technical controls. Security and business teams must work together.