Google’s Red Team Exposes Common Attacks on Artificial Intelligence Systems

Embracing an adversarial mindset, Fabian shared that the focus is on anticipating where real-world adversaries might strike next in the ML domain. Recently, Google’s AI red team released a report outlining the most prevalent TTPs that attackers employ against AI systems.

1. Adversarial Attacks on AI Systems: These attacks involve crafting inputs that deceive ML models, generating incorrect or unexpected outputs. The impact of successful adversarial examples varies based on the AI classifier’s use case.
2. Data-Poisoning AI: Data poisoning manipulates a model’s training data to corrupt its learning process. Detecting potentially poisoned data amidst the vast amount of online information has become crucial in countering this threat.
3. Prompt Injection Attacks: In these attacks, users insert additional content into text prompts to manipulate model outputs, potentially leading to biased or offensive responses. Striking a balance between input restrictions and user freedom is crucial.
4. Backdoor Attacks on AI Models: Among the most serious threats, backdoor attacks involve malicious code insertion into models, enabling hidden sabotage or data theft. Defending against these attacks requires machine learning expertise coupled with classic security best practices.

The red team concept, originating from military exercises, involves designated teams assuming an adversarial role against the “home” team. While traditional red teams offer a foundation, attacks on AI systems necessitate specialized AI knowledge. Fabian’s team leverages their AI expertise to stay ahead of adversaries, confident that their efforts will empower defenders against future attacks. In the long run, integrating AI models into software development life cycles promises to reduce vulnerabilities, providing a safer digital landscape.