ECCouncil Updated 312-50v13 Exam Questions and Answers by rogan

The capability described—administrators instantly provisioning servers, storage, and compute through a web portal without needing the provider to manually intervene—is the NIST cloud characteristic called on-demand self-service. In NIST’s cloud computing model, on-demand self-service means a consumer can unilaterally provision computing capabilities (such as server time and network storage) as needed automatically, without requiring human interaction with each service provider.

The scenario explicitly highlights that the admins can scale resources “instantly” through a dashboard and that there is “no manual involvement from the cloud provider.” That is exactly what on-demand self-service captures: rapid provisioning driven by the customer through automated orchestration and APIs/portals.

Why the other options are not the best match:

Broad network access (D) means cloud capabilities are available over the network and accessed through standard mechanisms by heterogeneous platforms (mobile, laptops, workstations). While the dashboard is accessed over the network, broad access is about reachability and standard access mechanisms, not the self-provisioning behavior.

Resource pooling (C) refers to the provider’s multi-tenant model where physical/virtual resources are pooled and dynamically assigned; it explains how the provider can offer elasticity, but the user-facing “provision it yourself” aspect is on-demand self-service.

Measured service (B) refers to metering and monitoring resource usage for billing/optimization; it doesn’t explain instant self-provisioning.

Therefore, the characteristic is A. On-demand self-service.

The correct answer is B. Password Spraying because the pattern described is the defining behavior of a spraying attack: the attacker tries a small set of common or likely passwords (for example, seasonal passwords, default patterns, or organization-themed guesses) across many different user accounts, using only a few attempts per account to avoid account lockout thresholds. The scenario explicitly states that “many accounts each receive only a few login attempts,” the attacker “reuses a very small set of likely credentials across a large number of accounts,” and the activity is “spread out over several days and IP ranges to avoid triggering automated lockouts.” These are the exact operational traits that distinguish password spraying from traditional brute force.

In CEH-aligned credential attack concepts, brute force is typically characterized by repeated attempts against a single account (or a small set of accounts), often cycling through many password candidates until the correct one is found. That approach is noisy and quickly triggers lockouts and detection. Password spraying flips the strategy: it keeps the per-account attempt count low and distributes attempts widely and slowly, which reduces alerting and lockout events. This is why the attacker was able to successfully access “several low-privilege accounts” before the pattern was noticed—spraying often compromises accounts with weak or reused passwords while staying below detection thresholds.

Why the other options are incorrect: Session hijacking involves stealing or replaying session tokens/cookies after authentication, not repeated login attempts across accounts. CSRF forces a logged-in user’s browser to perform unintended actions; it does not produce distributed authentication failures in logs. Brute force is related, but the avoidance of lockouts through minimal attempts per account and broad targeting is the signature of password spraying.

Therefore, the observed behavior most clearly indicates a password spraying attack.

CEH incident response material explains that LLMNR and NBT-NS poisoning is a common credential-harvesting technique in internal networks. These legacy name-resolution protocols operate via broadcast queries. Attackers can listen for these broadcasts and respond faster than legitimate DNS/hosts entries, impersonating the requested resource. Once the victim system attempts to authenticate, it sends NTLM hashes to the attacker-controlled machine. Tools like Responder and Inveigh automate this process, enabling attackers to collect challenge-response hashes for offline cracking. CEH highlights that this method is passive from the victim’s viewpoint and difficult to detect unless monitoring tools watch for anomalous LLMNR/NBT-NS responses. Options A, B, and D refer to other components of password cracking, but none describe the mechanism of capturing hashes via poisoned name resolution. The attacker’s technique directly aligns with exploiting the name-resolution protocol to intercept authentication attempts.

The CEH Web Application Hacking module identifies Session Fixation as a powerful session management attack that can bypass advanced authentication controls, including MFA.

In session fixation, the attacker forces the victim to authenticate using a session ID already known to the attacker. Once authentication completes, the attacker hijacks the valid session without needing credentials.

Option A directly targets session management logic.

Option B exploits authorization logic, not session handling.

Option C is unrelated to session management.

Option D is mitigated by encrypted cookies and HTTPS.

CEH explicitly warns that applications must regenerate session IDs after authentication.