Talk to Hack: The Rise of AI-Native API Vulnerabilities

By Bandana "HackWithHer" – Security Research Engineer, APIsec University Ambassador

In a world where APIs are everywhere—from your weather app to Siri to autonomous agents—how do we secure what we can’t even define properly anymore? Welcome to the age of AI-native APIs, where conventional defenses like WAFs and signature matching crumble under the weight of semantic payloads and unpredictable machine behavior.

In her standout session at APIsec|CON 2025, Bandana (“HackWithHer”) offers a bold, insightful look into this emerging frontier of cybersecurity. Here’s what you need to know.

The Problem with AI-Native APIs

APIs + LLMs = Risk Multiplier

Most people think of APIs as interfaces between applications. But with LLMs (Large Language Models) now consuming and triggering APIs, the line between interface and logic has blurred. That chatbot helping you book a flight? It’s not just calling APIs—it’s interpreting your intent and executing backend logic with minimal guardrails.

And the AI doesn’t know if your intent is good or malicious.

Real Threats Emerging from AI-Native APIs

1. Prompt Injection (Semantic Attacks)

Forget SQL injections—prompt injection uses natural language to manipulate AI models into leaking data, making unauthorized API calls, or breaking compliance barriers.

Obfuscation makes it worse:

• Base64 encoding
• Multilingual payloads (English, Hindi, French)
• Role-playing (“Act as an admin...”)

2. LLM Hallucination of Endpoints

AI assistants that invent API endpoints can trigger invalid but real calls. For example:

• A chatbot “hallucinates” a /deleteUser endpoint
• The call is passed to the backend
• If not properly validated, it could succeed

3. RAG Poisoning

Retrieval-Augmented Generation (RAG) integrates external data into LLMs. Poison that data, and the LLM will echo it—executing malicious prompts embedded in otherwise trusted documents or APIs.

4. Agentic Chains of API Calls

Autonomous agents like AutoGPT don’t just call one API—they chain multiple requests with evolving logic. One injection can cascade across APIs, leading to:

• Data leakage
• Function abuse
• Privilege escalation

Why Traditional API Security Falls Short

• WAFs can’t parse natural language.
• Rate-limiting entropy is practically impossible.
• Regexes fail when payloads become language-based and multilingual.
• AI outputs can change based on context, making signature detection unreliable.
• Dev teams rush to roll out AI products without AI-specific threat modeling.

What DevSecOps Teams Can Do Now

1. Understand the LLM Attack Surface

Study the OWASP Top 10 for LLMs. Start with:

• Prompt Injection
• Data Leakage
• Over-reliance on unvalidated outputs

2. Protect the API and the AI

Security must now be dual-layered:

• API layer: Auth, RBAC, input validation, endpoint allow-lists
• AI layer: Output moderation, semantic firewalls, RAG sanitization

3. Adopt Semantic Firewalls

Microsoft’s Prompt Shield is an example of a semantic firewall that inspects and filters natural language prompts. This is the future of LLM security.

4. Don’t Trust the Middleman

In an AI-native stack, your LLM is just a fancy middleman. Don’t trust it blindly. Apply zero-trust principles to all AI-driven decision making.

Final Words from Bandana

"You can't rate-limit entropy. And you can’t use regex to parse intent. This is the world we’re securing now.”

If adversaries are teaching AI to talk to hack, we must teach our APIs—and AI—to talk back smarter. AI-native applications are not just the future; they’re the present, and securing them requires an entirely new mindset.

Follow Bandana

Bandana (a.k.a. HackWithHer) is a rising voice in the API and AI security space, blending humor, hacking, and human-centered thinking. Connect with her to keep up with cutting-edge research:

• LinkedIn: HackWithHer
• Instagram: @hackwithher
• [Email: Available upon request]

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