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The AI Developer's Guide to Not Accidentally Summoning Skynet

GDE in Web Technologies and a Women Techmaker and Cypress Ambassador

AI-powered development tools are excellent for helping us deliver code more quickly. However, based on my extensive experience in test automation and identity management, I've noticed that these tools can also introduce subtle security issues that might even impress Skynet.

In this session, I will discuss real-world examples where AI assistants have inadvertently worked against developers, highlighting cases of data leaks, supply chain attacks, and prompt injection vulnerabilities. You will learn effective strategies to identify AI-generated security issues before they impact you. After all, if Skynet ever awakens, let's ensure it isn't due to an untested AI-generated function that set it off.

This talk has been presented at React Summit 2025, check out the latest edition of this React Conference.

FAQ

Skynet is a fictional AI from the Terminator movies that becomes self-aware and perceives humans as a threat, leading to a full-scale attack with killer machines.

AI in web development can introduce security risks such as leaking sensitive data, creating vulnerabilities, and being susceptible to prompt injection attacks.

A prompt injection attack involves a malicious user prompt that alters an AI model's behavior or output in unintended or harmful ways, bypassing its original instructions.

AI-generated code can introduce vulnerabilities if not reviewed properly, such as regular expression denial of service (redos) attacks, due to over-reliance on AI suggestions without proper vetting.

OWASP, or the Open Worldwide Application Security Project, provides a ranking of the top security risks in AI-assisted development, helping developers identify and mitigate potential threats.

Vetting AI-generated code is crucial to catch potential vulnerabilities and ensure security, similar to reviewing a junior developer's pull request before it reaches production.

Privilege escalation involves adversaries gaining higher level permissions through AI, exploiting system weaknesses and misconfigurations to bypass authorization controls.

Developers can prevent AI-related security threats by implementing strict vetting processes, using tools like Auth0 for authorization, and staying informed about evolving AI security practices.

The OWASP top 10 for LLMs highlights the most critical security threats in AI-assisted development, such as prompt injection attacks, helping developers focus on high-risk areas.

Developers should ensure AI-assisted development is secure by vetting code, implementing strong authorization controls, using filters to detect malicious prompts, and staying updated with AI security trends.

security

Ramona Schwering

12 min

17 Jun, 2025

Comments

Video Summary and Transcription

Introduction to the risks of AI in web development and the potential security threats it poses, drawing parallels to the fictional AI Skynet and emphasizing the importance of understanding and mitigating these risks. Discussion on the OVASP project revealing the top security risks in AI-assisted development, focusing on prompt injection attacks as a significant threat to LLMs. Explanation of prompt injection attacks in AI involving social engineering, role-playing to bypass AI safeguards, and data exfiltration risks, emphasizing the critical threat of privilege escalation in LLMs. Discussion on AI toolkit for authorization in Gen AI projects and the risks associated with over-reliance on AI-generated code, especially in the context of 'white coding' and regex vulnerabilities. Discussion on the risks of using AI-generated regular expressions without validation, the importance of manual review, code analysis, and human approval in AI-assisted development, emphasizing the need for security protocols and vigilance.

Available in Español: La Guía del Desarrollador de AI para No Invocar Accidentalmente a Skynet

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