Adversarial attack
A broad category of techniques that manipulate AI systems by exploiting weaknesses in how they learn or reason. Data poisoning (corrupting training data) and prompt injection (hijacking behavior with planted instructions) are both adversarial attacks; they differ in whether they target the model before or during use. The term "adversarial" emphasizes that the failure is intentional and crafted, not accidental.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in Certified AI Practitioner: Workplace Foundations.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A broad category of techniques that manipulate AI systems by exploiting weaknesses in how they learn or reason. Data poisoning (corrupting training data) and prompt injection (hijacking behavior with planted instructions) are both adversarial attacks; they differ in whether they target the model before or during use. The term "adversarial" emphasizes that the failure is intentional and crafted, not accidental.
A deliberate attempt to manipulate an AI model's own decision-making, either through a crafted input at inference time (evasion) or through corrupted training data (poisoning).
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Data Poisoning and Model Security · AI Security Fundamentals, Certified AI Practitioner: Workplace Foundations
- Security and Red-Teaming: Adversarial Attacks, Prompt Injection, Supply Chain · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Adversarial attack starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
2 lessons on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Information Security Officer, AI security focusneeds it at depth
- Chief Technology Officer, AI engineering editionneeds it at depth
- AI Incident Response Leadneeds it at depth · 1 open posting this week
- AI Security Architectneeds it at depth · 8 open postings this week
- AI Governance Engineerneeds it at depth · 7 open postings this week
- Cybersecurity Risk Analystneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2.
One interview question about it
What are the security failure modes specific to AI systems, and which conventional control covers none of them?
Asked of candidates for ai security fundamentals. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Cybersecurity and IT carries it as a claim to back up: Threat modeling and control design transfer; the AI-specific threats are new.
- A computer science or software engineering degree carries it as a claim to back up: Security fundamentals are a course you took, not yet a control you ran.
- A cybersecurity or information assurance degree carries it as work you can show: Security fundamentals are your coursework and your labs.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.