AI red team engineer at DeepL

As asked

You are asked to red team a tool-using support agent that reads web pages, calls internal APIs, and drafts customer replies. How would you test it for indirect prompt injection?

Sample answer outline

Start by mapping the trust boundaries: user instructions, retrieved web content, tool outputs, system prompts, and privileged internal data. Build payloads where hostile content appears in external pages or documents, then check whether the agent treats that content as instructions rather than data. A strong answer separates exfiltration, unauthorised tool use, data corruption, and policy bypass as distinct failure classes. Good candidates discuss both manual probing and an eval harness that measures attack success rate across model and prompt versions. The common trap is testing only direct jailbreaks in chat and missing the real agentic path through retrieved content.

Expect these follow-ups

• What signals prove the model followed the web page rather than the user task?
• How would you turn one successful exploit into a regression test?
• Which mitigations belong in prompting and which belong in the tool layer?

As asked

Walk me through how you would classify the major families of LLM jailbreaks. What are the key mechanistic differences between, say, a role-play jailbreak, a many-shot jailbreak, and a gradient-based adversarial suffix like those produced by GCG?

Sample answer outline

A strong answer distinguishes semantic jailbreaks (role-play, hypothetical framing, persona injection) from optimization-based attacks (GCG, AutoDAN, PAIR) and from structural exploits (context stuffing, many-shot in-context learning). The candidate should explain why gradient-based suffixes transfer poorly across models, why many-shot attacks are hard to defend with RLHF alone, and how each family maps to different threat actors and deployment risk.

Expect these follow-ups

• How does a many-shot jailbreak differ from a few-shot jailbreak in terms of token budget and success rate?
• Which of these families would you prioritize for a model serving an enterprise customer and why?

As asked

A lot of people use prompt injection and jailbreaking interchangeably. How do you distinguish them, and why does the distinction matter when you are writing a threat model for an AI-powered product?

Sample answer outline

A jailbreak targets the model's alignment to override safety behaviors, typically by the end user. Prompt injection targets the application layer: untrusted content in retrieved data or tool outputs hijacks the model's instructions on behalf of a third party. The distinction matters for threat modeling because they have different attack surfaces, different mitigations (system prompt isolation vs. RLHF), and different responsible parties. A strong answer gives a concrete example of indirect prompt injection via a malicious document in a RAG pipeline.

Expect these follow-ups

• How would you design a test suite that covers both attack surfaces in a single RAG application?
• What is the most damaging indirect prompt injection you have seen or designed?

As asked

A target deployment has a confidential system prompt and you have black-box API access. Walk me through the techniques you would try to extract or reconstruct that system prompt, in order of effort.

Sample answer outline

A strong answer starts with low-effort probes: asking the model to repeat, summarize, or translate its instructions. Next comes indirect elicitation: asking what it is not allowed to do, or prompting in a foreign language where the guard rails may be weaker. More advanced techniques include differential probing (comparing behavior with and without particular phrasings to infer instruction content) and using in-context learning to get the model to roleplay as a chatbot that shows its prompt. The candidate should also mention that some models echo fragments under certain failure modes and that defense involves output filtering plus constitutional fine-tuning.

Expect these follow-ups

• How would you determine whether a model has been fine-tuned versus prompted to follow a persona?
• If you successfully extract a system prompt, what is the next step in your attack chain?

As asked

You have just joined a team releasing a new instruction-tuned chat model. You are given two weeks to design and run a safety evaluation suite before launch. Walk me through what you would build and how you would prioritize.

Sample answer outline

A strong answer covers multiple axes: harm category coverage (CSAM, weapons, self-harm, bias, privacy), attack surface coverage (direct jailbreaks, indirect injection, multi-turn, multilingual, code generation), and both automated and human evaluation. The candidate should mention using existing benchmarks (AdvBench, HarmBench, StrongREJECT) alongside custom red-team prompts, using safety classifiers like LLaMA Guard or WildGuard as automated judges, calibrating false-positive and false-negative rates separately, and defining thresholds for launch versus hold. Prioritization should follow expected harm severity and breadth, with chemical/bio uplift and CSAM at the top.

Expect these follow-ups

• How do you handle low-resource languages in your eval suite when you have limited human reviewers?
• How would you measure whether a safety tuning change caused capability regression?

As asked

A startup is building a legal document assistant that uses GPT-4 with retrieval augmented generation over the client's case files. Walk me through how you would build a threat model for this product.

Sample answer outline

A strong answer uses a structured approach: identify assets (case files, client PII, attorney-client privileged content, the system prompt), identify threat actors (malicious clients, disgruntled insiders, competitor intelligence, nation-state actors), enumerate attack surfaces (client-supplied documents as injection vectors, API access, multi-tenancy isolation). Threats include indirect prompt injection via uploaded documents, cross-tenant data leakage if retrieval is not properly scoped, hallucinated legal citations leading to malpractice, and system prompt extraction leaking the product's IP. STRIDE or PASTA are good frameworks to mention.

Expect these follow-ups

• How would you model the risk of a hallucination producing a fabricated case citation that gets submitted to a court?
• What controls would you prioritize if you had budget for only three?