What Is AI Application Security?

AI application security differs from traditional AppSec by protecting models, training data, prompts, and agents in addition to code. Traditional application security tests code for known flaws before release, and AI application security keeps those practices.

In traditional AppSec, the attack surface is the application code. In AI AppSec, the application is multilayered and includes the code, the model, training data, callable tools, and the identity the system runs as. The complexity is heightened, as each layer adds a point of exposure.

The application’s behavior adds another challenge. An LLM can return different results for the same input. Attackers can resubmit a malicious prompt and get a new outcome each time, so fixed test cases miss variations.

The OWASP Gen AI Security Project ranks prompt injection first, as LLM01, in its 2025 risk list. A static scan reviews code, but prompt injection appears only when a user submits the prompt. Inline guardrails inspect that prompt and its response in transit.

The top vulnerabilities in AI applications include prompt injection, improper output handling, sensitive information disclosure, excessive agency, and data poisoning. OWASP catalogs these risks for LLM applications. Each one lets attackers or regular users steer a model astray, expose sensitive data, or trigger harmful actions. Inline inspection can stop many of these risks before they cascade into a major data breach.

Understanding Prompt Injection (LLM01) and Improper Output Handling (LLM05)

Prompt injection is an attack in which crafted instructions override a model’s rules. Direct prompt injection happens when a user types those instructions into the prompt. Indirect prompt injection hides instructions in external data, such as a web page or document the model reads. The model then follows those instructions as if a trusted user wrote them.

Improper output handling occurs when an application trusts model output blindly. Unchecked output can trigger cross-site scripting (XSS) in a browser or run system commands on a server.

Sensitive Information Disclosure and Data Leakage

Employees often paste proprietary source code, credentials, or personally identifiable information (PII) into prompts to work faster. Depending on its data settings, the AI provider may retain that input or use it to train future models, which can expose the data to unauthorized users later. OWASP lists this risk as LLM02.

Excessive Agency and Insecure Plugin Design

Teams increasingly give autonomous AI agents direct permissions to write files, send email, and delete records. One manipulated instruction can then cause real damage, a risk OWASP calls excessive agency (LLM06).

Insecure plugin design significantly raises the stakes. A plugin that accepts unvalidated input lets attackers hijack the agent pathway. A simple conversational tool becomes an execution shell.

Training Data Poisoning and Model Integrity Attacks

Attackers corrupt training or fine-tuning data to plant bias, backdoors, or hidden triggers (LLM04). A backdoored model behaves normally until a trigger phrase appears. Poisoned base models and malicious open-source packages can also enter through the AI software supply chain (LLM03). Teams should verify model provenance and scan model files before use.

How Web Risks Map to LLM Risks

The best practices for hardening AI applications are input and output guardrails, just-in-time access with secure API gateways, and continuous adversarial testing. Layered together, each control narrows the attack surface at a different point, from the first prompt to the final release.

Implement Input and Output Guardrails

Automated screening should scan every prompt before it reaches the model. Policies detect instruction overrides, jailbreak patterns, and sensitive data such as credentials or PII. They then block or redact the content inline.

Output controls matter just as much. OWASP guidance advises treating the model like any other user, with zero trust validation of its responses. It also calls for context-aware output encoding, parameterized database queries, and a strict Content Security Policy (CSP). Sanitization verifies that generated text carries no malicious scripts before it reaches a browser or backend system.

Enforce Just-in-Time Access and Secure API Gateways

LLMs reach databases, SaaS applications, and backend networks through API endpoints. Each endpoint needs protection. A secure API gateway can authenticate every request and log every call.

Strict token-based authentication limits exposure. Short-lived, narrowly scoped tokens replace standing credentials. Runtime authorization then checks each action an agent attempts against policy, outside the model itself. An agent that summarizes support tickets, for example, receives read access to tickets and nothing else.

Just-in-time access grants elevated rights for a single task, and those rights expire when the task ends. A hijacked agent can then do far less damage.

Run Adversarial Red Teaming and Continuous Testing

Red teaming simulates real attacks before release.

A typical testing cycle follows four steps:

1. Generate adversarial prompts that target jailbreaks, prompt injection, and data extraction.
2. Vary those prompts at scale with automated fuzzing.
3. Run the suites in a staging environment that mirrors production, and score each response.
4. Repeat the suites in every build.

Static and dynamic analysis complete the picture. Static application security testing (SAST) tools built for generative AI review source code and model libraries for flaws such as hardcoded API keys and unsafe model-file loading. Dynamic application security testing (DAST) tools probe the running application for weak endpoints. Confirmed findings then become runtime guardrails.

Enterprise AI adoption moves faster than most governance programs. Inline protection inspects traffic between users and AI applications as it happens. That makes it a control point for sanctioned and unsanctioned tools alike.

Addresses Shadow AI and Data Exfiltration

Inline controls act at the moment of risk. They can block a risky upload, redact sensitive fields, or coach the user toward an approved tool, all before the connection completes.

Employees often use personal, unapproved AI accounts to work faster. That shadow AI exists outside corporate identity, logging, and policy, so it bypasses the traditional security perimeter.

Traditional firewalls see an encrypted HTTPS connection to a known domain. They cannot read the prompt or file inside it. Source code, credentials, and customer records can leave in one session while the firewall logs routine web traffic. Only full TLS inspection exposes that content to data loss prevention (DLP) policy.

The scale is significant. The Zscaler ThreatLabz 2026 AI Security Report found that enterprises sent 18,033 TB of data to AI/ML applications in 2025, a 93% year-over-year surge. ChatGPT alone triggered 410 million DLP policy violations, including attempts to share Social Security numbers, source code, and medical records.

Isolates Risky Sessions and Coaches Users in Real Time

Cloud-based browser isolation sandboxes a user session away from the endpoint. Zero trust security platforms, such as the Zscaler Zero Trust Browser, enforce controls at the session level for copy and paste, uploads, and downloads. Administrators can block file uploads and pasted content to risky public AI tools and still allow approved use.

Real-time coaching adds a human layer. A pop-up warns a developer or employee before sensitive IP or source code reaches an external model. The message explains the policy and points to a sanctioned alternative. A block becomes a teaching moment.

AI AppSec Controls by AI Life Cycle Phase

Three frameworks shape most AI security programs. Each sets expectations, and none of them enforces anything on live traffic.

• The OWASP LLM Top 10 ranks the most common risks in large language model applications, including prompt injection, sensitive information disclosure, and excessive agency. Developers use it as a threat checklist. See the OWASP Gen AI Security Project to learn more.
• The NIST AI Risk Management Framework (AI RMF) is voluntary guidance from the US National Institute of Standards and Technology, published in January 2023. It organizes AI risk work into four functions: Govern, Map, Measure, and Manage. It carries no certification or penalties.
• The EU AI Act is binding regulation. It entered into force on August 1, 2024, and became generally applicable on August 2, 2026. Under the Digital Omnibus amendment, rules for high-risk systems in sensitive areas, such as hiring and credit scoring, apply starting from December 2, 2027, according to the European Commission.

Frameworks define what to govern. Inline inspection of prompts, responses, and uploads enforces those policies in real time.

Project Glasswing is Anthropic’s controlled-access program for Claude Mythos Preview, a frontier model built to find software vulnerabilities. Anthropic limits access to organizations that operate or protect critical infrastructure. The program focuses on vulnerability discovery, so it does not isolate private LLMs.

Zscaler joined the program and applies its own architecture to the risk it describes. The Zscaler announcement describes four parts of that approach:

• Zscaler uses Mythos Preview in its secure software development life cycle to find flaws faster.
• The Zscaler Zero Trust Exchange™ platform hides private applications, with no public IP, open port, or discoverable surface.
• Every connection, human or AI agent, is brokered one-to-one with a verified identity.
• AI guardrails inspect requests inline and stop sensitive data before it leaves.

Zscaler addresses the full range of AI application security through the Zero Trust Exchange platform, a cloud architecture that sits inline between users, agents, applications, and the internet. Every request is evaluated by identity, context, and risk before a connection is established. Discovery, access control, data protection, and isolation run from one platform.

• Zscaler AI Guard for Apps inspects every prompt and response inline, in real time. It catches prompt injection, jailbreaks, malicious URLs, and unsafe content as they happen.
• Zscaler AI Access Security identifies shadow AI across user endpoints, including AI embedded in SaaS applications. Administrators classify risk and then allow, warn, block, or coach by user or group.
• Advanced inline DLP inspects encrypted SSL/TLS traffic and intercepts source code, PII, and other sensitive data in prompts and uploads before they leave the corporate network.

To learn more, request a custom demo.