AI apps are multiplying across the digital ecosystem, bringing a multitude of benefits but also a range of new risks. App security teams are familiar with protecting “regular” apps, but now they need to adjust their defenses to address a whole new set of threats.
A number of risks are unique to AI applications and simply weren’t relevant for non-AI apps, primarily:
- Prompt injection attacks
- Model tampering
- Data leakage
- Harmful outputs
- Model confidentiality and IP theft
It’s vital for AI apps to contain purpose-built application security that protects them against these unique threats.
1. Prompt Injection Attacks
Prompt injection attacks like indirect prompt injection, guardrail bypass, and novel “jailbreak” phrasing exploit the way that AI systems interpret natural language as executable control input. Attackers craft prompts or embed instructions in retrieved content to override system rules, bypass safety guardrails, and/or manipulate tool use and agent behavior.
They’re particularly insidious because they don’t involve any kind of illicit systems access and it can be hard to distinguish prompt injection attacks from regular use. Prompt injection attacks can directly change app behavior to enable data leakage, unauthorized actions, policy violations, and downstream system compromise.
It’s crucial to bake in strict system prompts, input/output validation, and tool-call allowlists, and to continuously test defense against adversarial prompts and novel jailbreak patterns.
2. Data Leakage
Data leakage is the best-known threat for any kind of AI tool, and it should be taken extremely seriously. AI apps often handle personal, proprietary, or regulated data at scale, with sensitive data hiding within training data, prompt data, and embedding and vector stores. This makes standard application security controls insufficient on their own.
AI apps can leak data in ways that go beyond traditional breaches, such as outputs, embeddings, or logs, as well as cross-tenant and cross-session data exposure. AI data leakages are often more difficult to detect, quantify, or remediate.
To combat them, apply least-privilege access to prompts, embeddings, and logs. Data minimization, isolation, and privacy testing help prevent memorization and cross-tenant exposure.
3. Model Tampering
The models that run AI applications are another vulnerable target for security threats. Malicious users may try to manipulate the model itself, or attack the data and artifacts that influence its behavior. This can include training data poisoning, backdoored models, compromised pretrained weights, supply-chain attacks, and poisoning retrieval sources in RAG systems to effectively alter model outputs.
Model tampering can be hard to detect, but the consequences affect every user and downstream application. Malicious behavior that’s embedded in the model may survive updates and audits, undermining trust in model outputs and creating systemic risk across products that share the same model or data pipeline.
AI app security best practices include protecting the training and deployment pipeline with provenance checks, signed models, and trusted data sources, while monitoring for behavior drift or backdoors.
4. Harmful Outputs
AI systems are notorious for generating hallucinations, biased or discriminatory content, and unsafe recommendations. Yet AI systems are still widely seen as authoritative. Many users accept outputs and integrate them directly into decision-making workflows with zero or minimal fact-checking, making inaccurate outputs potentially very dangerous.
Incorrect or non-compliant outputs are a threat even when there’s no external entity trying to use them maliciously. False, biased, or defamatory information can cause real-world damage, legal exposure, and reputational harm, especially in high-stakes domains such as healthcare, finance, or security operations.
Protection against these outputs requires enforcing layered safety controls, domain-specific validation, and requiring human-in-the-loop review for high-risk actions or decisions.
5. Model Confidentiality and IP Theft
With model confidentiality and IP theft attacks, malicious parties try to extract, replicate, or reverse-engineer a proprietary AI model through systematic querying. It involves techniques like model extraction and distillation, which allow third parties to approximate the original model’s behavior without accessing its weights or infrastructure.
AI models frequently represent a company’s core intellectual property and competitive advantage. If someone else successfully extracts model capabilities, they can reuse them without training costs or licensing, resulting in direct financial and strategic loss.
To prevent model theft, it’s best to limit query rates and output fidelity, set up automated monitoring for extraction patterns, and apply contractual, technical, and watermarking controls wherever it’s feasible. Many of these measures can be accomplished by installing an advanced API management solution, a web app firewall (WAF) or a bot detector.
6. Unbounded Consumption and DoS
AI application availability is tied directly to compute and cost. This means that overuse or abuse of tokens, compute, tools, or downstream services can run up enormous costs very quickly, and/or result in resource exhaustion that makes the app unavailable.
Unbounded consumption can be deliberate and malicious, but it can also stem from unwitting users who put in oversized inputs, trigger recursive agent loops or tool-call amplification, or overly repeat requests. This makes this kind of denial of service attack both easy to exploit and difficult to contain.
Stopping unbounded consumption attacks calls for explicit safeguards such as rate limits, token and cost budgets, loop breakers, and tool-call caps, to prevent resource exhaustion and economic abuse. Many of these measures can be accomplished by installing an advanced API management solution, a web app firewall (WAF) or a bot detector.
7. Agent Autonomy Escalation
Agent autonomy escalation is what happens when AI agents accumulate excessive context, permissions, or decision authority over time, and then perform actions that go beyond what was intended or approved.
Like several other AI app threats, AI app or agent escalation can occur even without malicious intent, simply through long-horizon planning, chained actions, or misplaced trust.
Small errors or malicious inputs can compound into large, irreversible effects, particularly when agents interact with real-world systems, data stores, or operational workflows. Securing AI systems against agent escalation requires strong isolation and human oversight.
Securing AI Applications Requires a New Security Strategy
The new era of AI-powered apps calls for a new set of tactics and defenses for app sec teams. Baking in protections for the unique vulnerabilities of AI models, data interactions, resource availability, and agent decision-making is critical to keep AI apps available and delivering the required impact.
