AWS guidance treats prompt injection as an adversarial security problem in which malicious or manipulated input attempts to override the intended instructions of a generative AI application. Among the choices provided, adversarial prompting is the relevant technique because it deliberately exercises a model with malicious, conflicting, or manipulative prompts to expose weaknesses before those weaknesses are exploited in production.
An exact AWS security statement is: “You can use an Amazon Bedrock Guardrail to help protect against prompt injection attacks.” AWS further recommends testing generative AI applications against adversarial inputs and prompt-injection attempts so that developers can identify weaknesses in prompt handling and strengthen defenses.
Adversarial prompting is therefore best understood as part of adversarial testing and hardening . For example, evaluators might intentionally submit prompts that request the model to ignore system instructions, disclose protected instructions, change its assigned persona, or follow malicious instructions embedded in retrieved data. If the application responds incorrectly, developers can strengthen system prompts, input validation, Amazon Bedrock Guardrails, output validation, IAM restrictions, or tool-access policies.
Zero-shot prompting simply asks a model to perform a task without providing worked examples. Least-to-most prompting decomposes a difficult problem into progressively more complex subproblems. Chain-of-thought prompting encourages intermediate reasoning. These techniques can improve task performance or reasoning quality, but they are not principally security techniques for discovering prompt-injection vulnerabilities.
A critical distinction is that adversarial prompting by itself is not a complete runtime defense. AWS recommends defense in depth, including Guardrails, input sanitization, restricted permissions, validation, monitoring, and adversarial testing. Nevertheless, given the four options, A is clearly the AWS-aligned answer .
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