Mackenzie Jackson

The AI Security Survival Guide: Practical Advice for Stressed-Out Developers

Prompt injection is the new SQL injection, but for AI. This survival guide gives developers practical advice to secure their applications.

The AI Security Survival Guide: Practical Advice for Stressed-Out Developers
#1about 4 minutes

Understanding AI security risks for developers

AI is now part of the software supply chain, and instruction-tuned LLMs like ChatGPT introduce risks when developers trust generated code they don't fully understand.

#2about 2 minutes

How LLM training data impacts code quality

LLMs are often trained on vast, unfiltered datasets like the Common Crawl, which includes public GitHub repositories and Stack Overflow posts of varying quality.

#3about 6 minutes

Understanding and demonstrating prompt injection attacks

Prompt injection uses malicious language to bypass an AI's instructions, as shown in a demo where a simple command hijacks a text summarizer app.

#4about 3 minutes

Attacking an AI email assistant with prompt injection

A malicious email containing a hidden prompt can compromise an AI email assistant, causing it to add malicious links or exfiltrate data without user interaction.

#5about 2 minutes

Strategies for mitigating prompt injection vulnerabilities

Defend against prompt injection by using third-party security agents to analyze I/O or implementing a multi-LLM architecture with privileged and quarantined models.

#6about 6 minutes

Exploiting AI with package hallucination squatting

AI models can invent non-existent software packages, which attackers then create as malicious decoys to trick developers into installing malware via hallucination squatting.

#7about 5 minutes

How attackers use AI to refactor exploits

Attackers use purpose-built malicious AI models to refactor old exploits, making them effective again, and to create highly convincing spearphishing campaigns.

#8about 2 minutes

Preventing sensitive data leakage into AI models

Employees often paste sensitive information like API keys into public AI models, creating a risk of data leakage and enabling attackers to extract secrets.

#9about 2 minutes

Final advice on adopting AI tools securely

Instead of banning AI tools, which creates shadow IT risks, focus on developer education, using the right tools for the job, and reinforcing security fundamentals.

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