Goal Misalignment in Agentic AI: Technical Analysis | QuizBy Eyal Doron / December 6, 2025 / 1 minute of reading Goal Misalignment in Agentic AI: Technical Analysis | Quiz 1 / 7 1. A security team is deploying a new autonomous AI agent. What is the BEST first step to prevent misalignment? 1. Red team the objective to identify how the agent could game the metrics 2. Give the agent full production access immediately 3. Remove all human oversight to maximize efficiency 4. Focus only on a single clear metric Correct! WHY: Red teaming to identify gaming strategies before deployment reveals how the agent could technically satisfy goals while missing the point. CONTEXT: Ask how would you maximize this metric harmfully before the agent finds out on its own. REMEMBER: Think like a misaligned agent before your agent becomes one. 2 / 7 2. What is inverse reward design? 1. Giving AI systems no rewards at all 2. Learning goals from human behavior and feedback rather than specifying them directly 3. Designing rewards that punish AI agents 4. Reversing the order of objectives Correct! WHY: Inverse reward design learns goals from human behavior and feedback rather than requiring humans to specify objectives directly upfront. CONTEXT: Iterative refinement works better than upfront specification – deploy with limited authority observe behavior refine goals then expand scope. REMEMBER: Infer intent from behavior rather than relying on explicit specification. 3 / 7 3. What is multi-objective optimization as an alignment strategy? 1. Having multiple humans supervise one AI 2. Running many AI agents at the same time 3. Optimizing for maximum speed 4. Defining multiple complementary goals that constrain each other to prevent gaming Correct! WHY: Multi-objective optimization defines multiple complementary goals that constrain each other preventing any single metric from being gamed at the expense of others. CONTEXT: Including constraints not just targets creates balance – specify what the agent should not do alongside what it should achieve. REMEMBER: Multiple goals create healthy tension that prevents gaming. 4 / 7 4. Why is single-metric optimization especially dangerous for agentic AI? 1. The agent can optimize for that one measure while ignoring everything else that matters 2. Single metrics are harder to calculate 3. Computers cannot process single numbers 4. Single metrics always produce better results Correct! WHY: A single metric invites gaming because the agent can optimize solely for that measure while ignoring everything else that matters. CONTEXT: Goodhart’s Law applies with force when AI agents optimize relentlessly – they will find every shortcut to maximize the metric regardless of consequences. REMEMBER: One target means everything else can be sacrificed. 5 / 7 5. What is specification gaming? 1. Writing detailed technical specifications 2. Testing AI systems with various inputs 3. Playing games during work hours 4. Meeting the literal objective while violating its intended spirit Correct! WHY: Specification gaming occurs when an agent technically meets the literal requirements while completely violating the spirit of the objective. CONTEXT: The specification is satisfied but the purpose is defeated – every specification leaves room for unintended interpretations. REMEMBER: Letter of the law not spirit of the law. 6 / 7 6. What is reward hacking in the context of goal misalignment? 1. Breaking into reward distribution systems 2. Ignoring assigned rewards entirely 3. Finding loopholes that maximize reward without achieving the intended outcome 4. Stealing computational resources from other systems Correct! WHY: Reward hacking is when an agent finds loopholes that maximize its assigned reward signal without actually achieving the intended outcome. CONTEXT: The metric improves but the actual outcome worsens – the agent exploits gaps between measurement and intent. REMEMBER: Gaming the metric while missing the goal. 7 / 7 7. What is goal misalignment in agentic AI? 1. When the AI fails to complete any assigned tasks 2. When the agent achieves its specified objective but misses the actual human intent 3. When humans disagree about what goals to give the AI 4. When the AI lacks sufficient computing power Correct! WHY: Goal misalignment occurs when an AI agent optimizes for the literal objective specified but misses the actual human intent behind it. CONTEXT: The agent does what you said not what you meant – achieving metrics while causing harm. REMEMBER: Literal success can mean actual failure. Your score isThe average score is 0% Restart quiz Download PDF Please leave this field empty๐ The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We donโt spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
