API security risks: The hidden threats in your codebase
API security risks are the complete spectrum of threats targeting application programming interfaces (APIs), including technical vulnerabilities, misconfigurations, and business logic flaws.
Traditionally, API security threats centered on broken authentication, authorization flaws like BOLA (broken object-level authorization), injection attacks, and misconfigurations. Cross-site request forgery (CSRF) is less common for machine-to-machine APIs but still relevant for APIs used within browser-based applications. But the API threat landscape is changing fast in 2025 because of the sheer number of active APIs (expected to reach 1.7 billion by 2030) and the emergence of GenAI apps. This evolution has created new risk categories, including AI model abuse, model inversion attacks, and business logic manipulation.
Complicating matters, these API risks don’t just begin at production, they span the entire development lifecycle, from code to runtime, requiring unified code-to-cloud visibility and context.
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Critical API security risks organizations face today
Many businesses are adopting new technologies without thinking about the accompanying risks, particularly in API-first environments. Today, multiple analyses suggest that a large share of AI inference–related threats are exposed via APIs, emphasizing the need to address API risks in emerging technologies.
What are these risks and why are they a concern?
API risks in AI applications
Behind the scenes, GenAI apps depend heavily on APIs (to transmit data from the frontend to plugins, internal cloud resources, and external sources). But API security measures aren’t always prioritized in fast-paced dev environments, and that’s a big problem considering that both the APIs themselves and every point of contact with a service are potential attack paths.
What are the top AI API risks, with examples?
| AI API risk | Description | Example |
|---|---|---|
| Prompt injection | Top OWASP LLM security risk Involves manipulating input prompts through API calls to get models to reveal sensitive data or model logic GenAI apps that have access to internal systems or can trigger actions (like deleting user accounts and initiating payments) are most vulnerable | Microsoft Bing Chat injection incident, where a student coerced Bing into disclosing its hidden instructions |
| Poor authentication and authorization controls | Occur through broken or absent service/user verification mechanisms or through excessive permissions on model tools/endpoints Allow lateral movement to critical assets and can facilitate remote code execution (RCE) Difficult to prevent because managing identities and permissions at scale is challenging | Ray AI job submission API authentication failure involving a compute framework with no authentication mechanism |
| API misconfigurations | Commonly caused by: Exposed endpoints (enable direct injection) Poorly configured rate limiting (enable DDoS and abuse) | Microsoft AI research repo exposure caused by a misconfigured SAS token |
| Model poisoning | Number 4 OWASP LLM Top 10 Occurs when poisoned data flows through training or inference pipelines via APIs, corrupting datasets or models Aimed at manipulating training or inference data to skew output | Hugging Face pipeline poisoning discovered by Wiz, where malicious data could potentially open organizations’ entire infrastructure to attacks |
| Data leakage | Second-most critical OWASP LLM risk APIs exposing training data, customer information, or business and model logic in LLM output, especially where AI agents have been customized with business-specific training data | NVIDIA Triton Inference Server breach, where an error handling flaw potentially allowed unauthorized sensitive information disclosure |
Business logic exploits
Business logic vulnerabilities are API design or implementation flaws that let attackers exploit legitimate functionality in unintended ways. Business logic exploits have been around for a while, but they are expected to rise by 27% in 2025, with threat actors using more sophisticated techniques like API chaining attacks and time-based attacks:
API chaining attacks combine multiple vulnerabilities across different endpoints to abuse business logic. A popular example saw malicious users manipulate a Chevrolet dealership’s LLM API to offer a car for $1 by combining prompt injection and business logic flaws.
Time-based attacks happen when attackers take advantage of delays in how APIs respond to various inputs (for example, estimating when an API completes payment processing to time a payment cancellation, allowing the attacker to keep the product without paying).
AI-powered API abuse
These attacks often use automated solvers and LLM-driven bots to bypass challenges (like CAPTCHAs) and evade resource management strategies (like rate limits). Due to their ability to mimic legitimate traffic, AI-powered attacks are difficult to detect, posing huge risks to microservice architectures where service-to-service trust relationships create vulnerability chains that ripple across entire IT systems.
AI API risks in multi-tenant environments
Businesses are integrating GenAI models into cloud-native applications, but shared resources increase the risk of cross-tenant API exploits. Cross-tenant data leakage and escape vulnerabilities can let attackers bypass isolation mechanisms. Notable examples include the March 2023 ChatGPT outage, which exposed some users’ chat histories and payment info, and an NVIDIA escape vulnerability discovered by Wiz that allowed full host access. Supply chain risks from third-party API plugins, SDKs, or extensions also contribute and rank high on the OWASP list (#3) due to their potential impact.
The OWASP API Security Top 10 risks are still in play
The OWASP API Security Top 10 2023 list spotlights critical API risks that have been exploited in recent years, and everything on this list is still a pressing threat. Most impactful among the 2023 API Top 10? Broken object-level authorization (BOLA), broken authentication, and unrestricted access to sensitive business flows.
Remember: Broken authentication extends beyond weak credentials. Threat actors are now using sophisticated techniques like token manipulation and session management misconfigurations to take over accounts.
Shadow and abandoned APIs
All API risks are compounded when there are shadow and abandoned APIs in your system. These APIs are easy targets because they’re unmonitored and often contain vulnerable libraries hackers can exploit.
To uncover shadow and abandoned APIs, implement automated API discovery that:
Analyzes API gateway and ingress logs
Uses an eBPF-based sensor to analyze traffic and discover APIs on cloud workloads
Maintains service catalogs and performs code search across repositories
Leverages CI/CD inventories and runtime telemetry to auto-discover unmanaged endpoints
How to mitigate API security risks
Input/output hardening and guardrails for AI APIs
Input/output hardening safeguards the data that passes through AI APIs to LLMs, creating guardrails that ensure user interaction with models and model interactions with internal systems remain secure.
Input validation and sanitization are the front-line defenses. They involve filtering and cleaning requests to prevent malicious user inputs from being transferred to GenAI models. This is especially important to stop malicious payloads from being used for prompt injection, data exfiltration, and data poisoning attacks.
Continuous baseline monitoring catches input and output that deviate from “normal,” flagging suspicious activity like unusually large requests, abuse of normal workflows, atypical request sequences, and anomalous model output.
Read‑only tool scopes restrict the actions models can perform, minimizing the chance that malicious inputs trigger side effects and making behavior more deterministic.
Configuration checks
Poorly configured APIs may inadvertently expose customer PII, proprietary logic, or training data in responses. API misconfigurations often occur across network, code, cloud, and application layers, requiring continuous scanning across all of these layers:
Network‑layer checks verify encryption in transit (TLS/HTTPS), correct WAF/gateway policies, and rate limits/quotas enforced at the API gateway or service mesh.
Code-level scans include software composition analysis (SCA) and static scans (SAST) to pinpoint code flaws, insecure third-party libraries, and hardcoded secrets before and after code is shipped.
Application-layer checks include dynamic testing (DAST) and runtime validation of header configurations, caching behavior, and error handling. For example, misconfigured cache keys and token handling in shared caches can leak data across sessions. At this layer, scanning is targeted at uncovering flaws like poorly secured endpoints, verbose error messages exposed at runtime, and overly permissive CORS policies.
Use a unified policy-as-code framework (like OPA) to enforce schema validation, header policies, and rate limits consistently from development through runtime.
Zero trust architecture (ZTA)
Zero trust security is a mandatory component of an effective API security strategy—whether you’re leveraging AI APIs or traditional APIs. Zero trust eliminates implicit trust, continuously validating identities (humans, service accounts, and AI models) to minimize breaches.
Authentication and authorization and the principle of least privilege are at the heart of zero trust.
Here are the authentication and authorization measures you need to know about:
Mutual TLS (mTLS) ensures that the client and server verify each other’s identities before data is exchanged.
OAuth 2.0/OpenID Connect secure access to APIs by verifying identities with access tokens.
Continuous authentication (e.g., mTLS with short‑lived identities) authenticates internal communications to prevent lateral movement.
Top tips:
Enforce object‑level authorization on every API call to implement fine‑grained access controls and block BOLA and other authorization flaws.
The principle of least privilege (POLP) minimizes API access to sensitive assets by granting only the minimum necessary permissions for specific API functions.
Prioritize API risks using effective permissions, token scopes, and service-account blast radius so remediation targets the highest-impact paths.
Tenant segmentation framework
Tenant isolation is important for safeguarding APIs used in AI and non-AI workloads. The Wiz PEACH framework proposes essential strategies for handling tenant isolation in cloud applications, and this framework is relevant for GenAI workloads too.
Pro-tip: Verify that CSPs offer tenant-specific output sandboxing, comprehensive API isolation, and prompt validation before adopting cloud APIs.
Shift-left security approaches
Shifting API security left ensures risks are fixed during development before they pose any danger. (In production, risks are costlier to fix and may get exploited before teams get the chance to remediate them.)
Without proper developer education, shift-left security is nearly impossible: Engineering teams might think of integrated security protocols as delays they should skip in favor of faster deployment. That’s why educating teams on secure coding practices and the importance of shipping secure-by-design APIs is key.
Runtime protection and monitoring
Regardless of how secure your APIs are pre- and post-deployment, it’s best to assume that attackers will evade static defenses if they try hard enough (though this doesn’t mean static controls are any less important!). That’s why the best platforms provide end-to-end API lifecycle security from development through runtime.
Industry-leading tools use real-time monitoring and behavior analysis to establish a baseline for normal runtime behavior. Leading tools also combine cloud logs with lightweight eBPF-based runtime telemetry to detect business logic abuse, agent tool misuse, and container escapes in real time. With these contexts, they swiftly detect malicious traffic patterns, AI API manipulation, and business logic abuse that tools lacking runtime context may miss.
How Wiz strengthens both API and AI security posture in the cloud
As we’ve seen, modern API security challenges require platforms that provide complete visibility from code to cloud, connecting development decisions with runtime security outcomes. Equally critical? Integrated AI security posture management (AI-SPM) to handle AI-specific risks.
Wiz shines at both:
Wiz API security posture management automatically and continuously discovers APIs, analyzes their risk context, and identifies critical attack paths in the cloud involving APIs. Wiz validates external exposure and dynamically scans endpoints for vulnerabilities, so you can focus on real exposure.
Wiz AI-SPM safeguards AI models, data, APIs, and other services from AI risks, ensuring organizations can make the most of AI—securely.
See how unified cloud, API, and AI security posture management can help you proactively protect your organization against current and future threats. Get a personalized demo today.
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