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AI application security safeguards AI-powered apps against threats like prompt injection, context poisoning, and runtime exploits. Unlike traditional AppSec, it must cover models, context pipelines, and orchestration layers, in addition to code. New runtime-native approaches, such as Application Detection & Response (ADR), provide real-time protection that keeps AI systems secure and resilient.

What Is AI Application Security?

AI Application Security is the discipline of protecting applications powered by artificial intelligence from attacks, misuse, and vulnerabilities. Unlike traditional application security, which focuses on code and infrastructure, AI security must also account for risks introduced by prompts, training data, orchestration layers, and runtime behavior. The goal is to ensure that AI systems produce reliable, secure, and compliant outputs in real-world environments.

What Is an AI Application?

An AI application is any server-side software system deployed within a customer’s infrastructure or cloud environment whose functionality depends on executing logic for AI operations, particularly those involving large language models and their orchestration.

AI applications can take multiple forms depending on how deeply AI logic is integrated into their architecture:

Applications built using AI: Traditional software developed with the help of AI tools, such as code assistants or test generation systems.
SaaS applications using AI: Cloud-based products that incorporate AI functionality through APIs or third-party models, including chatbots, analytics assistants, and content recommendation engines.
Hosted applications using AI: Systems that execute AI logic directly within their runtime environment, such as model hosting, querying, or orchestration proxies. These are Miggo’s core focus area, as they present the highest exposure to runtime attacks, data leakage, and misconfiguration risks.

Why is AI Application Security Important Today?

The rise of generative AI, large language models (LLMs), and agentic AI systems has created new attack surfaces that organizations can’t ignore. Prompt injection, context manipulation, and model hijacking are already being exploited in the wild. Left unprotected, AI-driven applications can leak sensitive data, produce harmful outputs, or make business-critical mistakes.

Another growing concern is Shadow AI. Developers integrating unapproved or unsupervised external AI assistants or models into application workflows without governance can unintentionally expose sensitive data, violate compliance requirements, or introduce unvetted code into production environments. This includes cases where external interfaces of APIs silently invoke AI logic that has not been approved, documented, or monitored, expanding the attack surface without visibility.

Even sanctioned AI systems pose challenges due to their non-deterministic behavior. Unlike traditional software, the same input can yield different outputs, making it difficult to predict, test, or reproduce outcomes. This unpredictability increases the risk of logic errors, data leakage, and inconsistent business decisions.

The business impact is far-reaching: noncompliance with regulations, financial losses, reputational damage, and customer mistrust. With AI adoption accelerating across sectors like healthcare, financial services, retail, and technology, AI application security is no longer optional but essential.

What are the Key Risks to AI Applications?

AI systems introduce a unique blend of software, data, and human interaction risks that traditional security models weren’t built to handle. The OWASP Top 10 for Large Language Model Applications highlights many of these emerging threats. Below are some of the most critical risks that Miggo tracks and mitigates in real-world environments.

Prompt Injection

Prompt injection is one of the most widely recognized risks in AI security. Attackers manipulate the instructions fed to a model to override its intended behavior. This can lead to sensitive data exposure, system misuse, or the generation of harmful content. Because these manipulations often look like legitimate input, traditional defenses struggle to catch them.

Context Poisoning

Context poisoning occurs when attackers alter the data pipelines or sources that provide information to AI systems. If a model ingests poisoned context, its outputs can be corrupted, misleading, or even dangerous. This type of attack is especially concerning because the corrupted context often blends seamlessly into otherwise trusted workflows.

Supply Chain Risks

Many AI applications rely on pre-trained models, external APIs, and third-party libraries. Each of these components can harbor vulnerabilities or hidden backdoors. A compromised model or dependency can create systemic weaknesses that ripple across every application using it, making supply chain security a critical challenge. To manage these risks effectively, organizations benefit from maintaining an AI-specific software bill of materials that inventories models, datasets, prompts, and orchestration components, providing the transparency needed to assess exposure when supply chain vulnerabilities emerge.

For example, Miggo’s analysis of the September 2025 Shai-Hulud campaign in the npm ecosystem revealed how attackers weaponized popular open-source packages with malicious post-install scripts. These scripts exfiltrated publishing tokens, enabling the malware to republish itself in worm-like fashion across hundreds of libraries. The incident showed how easily trusted dependencies can be turned into attack vectors and why runtime detection is essential to complement static scanning in defending against supply chain compromises.

Insider or Misuse Risks

Not all threats come from external adversaries. Employees, contractors, or malicious insiders can exploit AI systems by exfiltrating data or bypassing governance controls. Even well-meaning users may misuse AI applications in ways that put sensitive data at risk, highlighting the need for continuous monitoring and access controls.

Runtime Exploits

AI systems often process requests that appear legitimate but, when chained together, create malicious outcomes. Runtime exploits leverage this blind spot, allowing attackers to bypass traditional perimeter defenses. Without runtime visibility into how the application and AI model interact, organizations may never detect these subtle but dangerous manipulations.

For example, Miggo’s research on the SharePoint ‘ToolShell’ attack chain showed how attackers bypassed recent Microsoft patches and exploited unsafe deserialization at runtime to gain persistent remote access. Even patched environments were vulnerable within days — highlighting why runtime visibility is critical.

AI Drift

AI drift occurs when a model’s behavior changes over time due to shifts in data patterns, usage contexts, or environmental conditions. Even without explicit poisoning, these gradual changes can lead to output degradation, inconsistent reasoning, or incorrect decisions that are difficult to detect through static testing. Because drift often emerges only during real-world operation, organizations need continuous runtime monitoring to identify abnormal model behavior before it impacts reliability or security.

How does AI Application Security Differ from Traditional AppSec?

While traditional application security is focused on vulnerabilities in code, configuration, and APIs, AI application security must protect a much broader surface. This includes the model itself, the prompts and context it consumes, and the orchestration layers that connect AI to business logic.

The fundamental difference is that AI systems behave non-deterministically. The same input can yield different outputs, meaning many risks cannot be detected during development or testing. As a result, traditional shift-left approaches fall short. True protection requires observing the model behavior in real time, after it has been deployed and is interacting with users, data, and systems.

Dimension	Traditional Application Security	AI Application Security
Focus	Code vulnerabilities, libraries, APIs	Model behavior, context pipelines, prompts
Tools	Static/Dynamic testing, WAFs, SAST/DAST	Runtime visibility, prompt validation, context monitoring
Risks	SQL injection, XSS, misconfigurations	Prompt injection, data leakage, model hijacking

Dimension:
Focus

Traditional Application Security:
Code vulnerabilities, libraries, APIs

AI Application Security:
Model behavior, context pipelines, prompts

Dimension:
Tools

Traditional Application Security:
Static/Dynamic testing, WAFs, SAST/DAST

AI Application Security:
Runtime visibility, prompt validation, context monitoring

Dimension:
Risks

Traditional Application Security:
SQL injection, XSS, misconfigurations

AI Application Security:
Prompt injection, data leakage, model hijacking

What are best practices for AI Application Security?

Securing AI applications requires a proactive, defense in depth strategy that goes beyond traditional AppSec methods. Because threats can emerge from prompts, context pipelines, third-party models, or runtime behavior, organizations need practices that balance prevention, detection, and governance. The following best practices provide a foundation for building AI systems that are not only high-performing, but also resilient, trustworthy, and compliant.

Curate context carefully: Treat context with the same rigor as production code.
Monitor in real time: Implement runtime attack detection and response to detect manipulations as they happen.
Adopt zero-trust principles: Validate every input and output, regardless of source.
Secure the supply chain: Rigorously vet models, APIs, and third-party libraries, supported by runtime vulnerability prioritization to focus on the most critical risks.
Integrate governance: Extend compliance frameworks like GDPR or HIPAA to AI systems, aligning with sector-specific needs in secure AI applications for regulated industries.
Add AI SBOM practices: Maintain a complete software bill of materials for AI components, including models, datasets, prompts, and orchestration tools, to ensure traceability and accountability across the AI stack.
Detect AI drift: Continuously monitor for changes in model behavior, output quality, and data distributions that may indicate drift, enabling early detection of anomalous or degraded responses.
Identify AI usage at external interfaces: Map and validate which external APIs, plugins, or integrations invoke AI logic to ensure unapproved or hidden AI functionality does not enter the attack surface.

How does Miggo’s ADR approach secure AI applications?

Miggo’s Application Detection & Response (ADR) is embedded directly inside applications, giving it a front-row seat to every AI request, response, and interaction. This runtime-native approach enables Miggo to spot hidden threats that perimeter defenses miss, including prompt injections and chained runtime exploits.

A foundational element of ADR is comprehensive application mapping to uncover Shadow AI. Miggo’s AppDNA technology provides deep, runtime-native visibility into every AI component running inside an application, revealing where AI logic exists, how it interacts with data, and whether unapproved or unmonitored models are being used. This discovery process helps organizations detect embedded AI, unauthorized API calls, and hidden dependencies that expand the attack surface.

Because AI systems behave non-deterministically, new risks can emerge only when the application is running. Miggo continuously identifies, observes, and responds to these unpredictable behaviors in real time, turning AI’s inherent variability into a detection advantage by recognizing abnormal patterns the moment they occur.

By establishing a behavioral baseline of normal application and model activity, ADR can immediately detect deviations, isolate suspicious patterns, and block malicious requests in real time before they escalate.

Because Miggo operates at the application layer, it protects not just the code but also the AI models, orchestration tools, and business logic that modern applications rely on. The result is trusted AI applications that are resilient, compliant, and secure by design.

Reach out to our team to learn how our solutions can provide the visibility and control you need to secure your data against hidden threats.

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