Snap·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Snap ML engineer interview, one technical question about LLM security. Pretty niche topic and I wasn't fully prepared for how deep it could go.

Questions Asked (1)

How are large language models vulnerable to adversarial attacks?

Technical Trade-offsSystem Design

Author's notes

I started with prompt injection since that's the most obvious one, then moved into data poisoning during pretraining.

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AI HintsAI Generated

Suggested Approach

Start by defining adversarial attacks in the context of LLMs, then systematically cover the main vulnerability types (input manipulation, prompt injection, data poisoning, model extraction) and their implications. Finally, discuss mitigation strategies and trade-offs, tying them to real-world systems like Snap's content recommendation or ad targeting.

Pro tip: Emphasize that adversarial robustness is not just a security concern but also a reliability and trust issue; mention that at scale, even low success rates can have significant impact, so defense-in-depth is crucial.

1. Define adversarial attacks on LLMs

Briefly explain what adversarial attacks are: inputs designed to cause the model to produce incorrect or harmful outputs. Distinguish between white-box and black-box settings.

2. Enumerate key vulnerability types

Cover input-based attacks (e.g., prompt injection, adversarial suffixes), training-time attacks (data poisoning, backdoors), and inference-time attacks (model extraction, membership inference).

3. Discuss real-world impact and examples

Relate vulnerabilities to potential consequences in Snap's context, such as manipulated content moderation, biased recommendations, or privacy leaks.

4. Outline mitigation strategies

Describe defenses like adversarial training, input sanitization, output filtering, differential privacy, and monitoring for anomalous inputs. Mention trade-offs between robustness, cost, and performance.

5. Conclude with system design considerations

Summarize how to integrate defenses into an ML pipeline, emphasizing continuous evaluation and red-teaming. Highlight the need for a balanced approach given Snap's scale and latency constraints.

Key Points to Mention

Prompt injection and jailbreaking techniques (e.g., role-play, encoding tricks)
Adversarial examples in text (e.g., synonym substitution, character-level perturbations)
Data poisoning and backdoor attacks during fine-tuning
Model extraction and stealing via API queries
Defenses: adversarial training, input validation, output moderation, and anomaly detection
Trade-offs: robustness vs. accuracy, latency, and computational cost

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.