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# Hacking AI - how attackers impose their will on AI

A simple printed sticker can trick an autonomous vehicle into dangerous misclassifications. Discover how attackers manipulate AI with mathematical noise, and learn to secure your machine learning pipelines.

- **Speakers:** Mirko Ross
- **Event:** World Congress 2023
- **Published:** November 10, 2023
- **Duration:** 27:02
- **URL:** https://www.wearedevelopers.com/videos/824-hacking-ai-how-attackers-impose-their-will-on-ai

## Summary

AI systems function as highly complex statistical black boxes, making them just as vulnerable to targeted intrusion as traditional software architectures. Currently, the artificial intelligence industry is largely repeating software engineering mistakes from the 1990s by inherently trusting unverified input data, inadvertently opening the door for systemic manipulation. Attackers frequently exploit this implicit trust through data poisoning methodologies, directly manipulating training sets to insert hidden backdoors or executing adversarial attacks that inject carefully calculated mathematical noise into live environments. For instance, precise visual disturbance patterns applied to physical stickers or 3D-printed objects can easily deceive public surveillance algorithms or autonomous vehicle cameras, tricking neural networks into dangerous misclassifications. Defending against these specific spatial vulnerabilities requires active data de-poisoning workflows before model inference occurs. Developers must treat all visual inputs suspiciously, utilizing proactive mitigation strategies like reducing image bit depth, leveraging auto encoders, or rotating pictures to logically disrupt the attacker's underlying noise pattern before processing. Furthermore, mitigating generative architectural threats like prompt injection remains extraordinarily difficult due to the near-infinite scale of possible linguistic inputs. When organizations attempt to manually patch these exploits with rudimentary content filters, they inevitably introduce severe scaling bottlenecks and manually encode human biases into the system limiters. Ultimately, safeguarding machine learning infrastructure demands rigorous operational input sanitization, strongly reinforcing the core cybersecurity truth that organizations cannot fully trust any AI model pipeline they have not strictly trained themselves.

**Keywords:** ai data poisoning, adversarial disturbance patterns, neural network manipulation, llm prompt injection, ai security vulnerabilities, input data sanitization, image bit depth reduction, autonomous vehicle machine vision evasion, ai de-poisoning techniques, machine learning threat modeling, training data backdoors, content filter human bias, three-dimensional adversarial attacks, ai surveillance evasion techniques, statistical black box debugging

## Chapters

1. **Core principles of manipulating artificial intelligence models** (00:03) — How the statistical nature of AI systems leaves them susceptible to data poisoning and unexpected outputs.
1. **Missing input validation practices in the artificial intelligence industry** (04:59) — Why failing to sanitize training data creates security vulnerabilities similar to legacy web application flaws.
1. **Exploiting image recognition systems with physical adversarial attacks** (08:03) — How adding calculated mathematical disturbance to two dimensional objects forces neural networks into dangerous misclassifications.
1. **Executing adversarial attacks on three dimensional physical objects** (15:18) — How embedding disturbance patterns into physical models guarantees misclassification across different camera angles.
1. **Defending visual artificial intelligence models against data poisoning** (17:13) — How reducing image bit depth effectively strips malicious mathematical noise from visual inputs.
1. **Prompt injection vulnerabilities and contextual mitigation testing challenges** (19:14) — Why complex contextual prompts easily circumvent manual ethical constraints and scale poorly for foundational models.
1. **Generating adversarial patterns and defending consumer machine learning models** (24:59) — How automated systems generate disturbance patterns and why utilizing third-party models remains inherently risky.

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- [Corrupting AI models and software supply chains](https://www.wearedevelopers.com/videos/715-a-hundred-ways-to-wreck-your-ai-the-in-security-of-machine-learning-systems) (from "A hundred ways to wreck your AI - the (in)security of machine learning systems")
- [Emerging risks and attack vectors in AI systems](https://www.wearedevelopers.com/videos/1948-building-trustworthy-ai-in-industry-beyond-traditional-cybersecurity) (from "Building Trustworthy AI in Industry: Beyond Traditional Cybersecurity")
- [Misusing AI for malware generation and physical evasion](https://www.wearedevelopers.com/videos/715-a-hundred-ways-to-wreck-your-ai-the-in-security-of-machine-learning-systems) (from "A hundred ways to wreck your AI - the (in)security of machine learning systems")
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- [Top security vulnerabilities for AI applications](https://www.wearedevelopers.com/videos/1637-delay-the-ai-overlords-how-oauth-and-openfga-can-keep-your-ai-agents-from-going-rogue) (from "Delay the AI Overlords: How OAuth and OpenFGA Can Keep Your AI Agents from Going Rogue")
- [Core AI security risks and data poisoning](https://www.wearedevelopers.com/videos/1217-can-machines-dream-of-secure-code-emerging-ai-security-risks-in-llm-driven-developer-tools) (from "Can Machines Dream of Secure Code? Emerging AI Security Risks in LLM-driven Developer Tools")

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