Free Resource

AI Security Guide for Startups

Updated for April 2026

Building with AI is easy. Securing it is not optional.

This free AI security guide covers the five critical threat categories facing startups building with AI: LLM prompt injection, model API security, training data poisoning, AI supply chain vulnerabilities and output validation failures. Each section includes attack examples, mitigation strategies and references to the OWASP Top 10 for LLM Applications.

Every startup shipping an AI-powered product faces security risks that traditional application security does not cover. This guide is built from vulnerabilities we find repeatedly in AI product security assessments. The first three sections are visible below.

The Guide

AI Security Threat Categories

01 - Prompt Injection

LLM Prompt Injection

Prompt injection is the most critical risk for any application using large language models. An attacker crafts input that causes the model to ignore its system prompt and execute unintended instructions. This can lead to data exfiltration, unauthorized API calls or bypassing access controls entirely.

Direct injection
User input directly overrides system instructions
Indirect injection
Malicious instructions embedded in external data sources the model retrieves
Mitigation
Input sanitization, output filtering, privilege separation between model and tools, human-in-the-loop for sensitive actions
02 - Model API

Model API Security

Your model inference endpoints are API surfaces that need the same security controls as any other API. Rate limiting, authentication, input validation and output sanitization are not optional. Unsecured model APIs enable denial-of-service attacks, prompt extraction and unauthorized access to model capabilities.

Common gaps
Missing rate limits, no auth on inference endpoints, API keys in client-side code
Mitigation
Server-side API key storage, per-user rate limiting, input length limits, request logging
03 - Data Poisoning

Training Data Poisoning

If you fine-tune models or use retrieval-augmented generation (RAG), your training data and knowledge base are attack surfaces. Poisoned training data can embed backdoors that activate on specific inputs. Compromised RAG sources can inject malicious instructions into model context at inference time.

Attack vectors
Contaminated fine-tuning datasets, compromised RAG document stores, poisoned embeddings
Mitigation
Data provenance verification, input validation on RAG sources, anomaly detection on model outputs

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