CyberCertPrepAI Security FundamentalsAdversarial ML & Model AttacksEASYFree question
Scenario
A job seeker who has been consistently rejected by automated resume screening systems reads a viral LinkedIn post describing a trick: paste a large block of relevant keywords in white-colored 12pt font at the bottom of the resume PDF. The text is invisible to human reviewers but readable by ML models extracting text from PDFs, boosting the resume's score above the rejection threshold.
A company's HR platform uses an ML resume screening tool. A job seeker reads a blog post claiming that adding certain invisible keywords (white text on white background) to a resume PDF causes ML screening tools to score the resume higher. This is an example of:
- A.A membership inference attack - the keywords signal the applicant was in the training set, since GPU memory is zeroed between inference calls by the CUDA driver itself
- B.An evasion attack - manipulating the input at inference time to cross the model's decision boundary
- C.A backdoor attack - the invisible text is a trigger
- D.Data poisoning - adding the keywords permanently corrupts the model
Why B is correct