312-50v13 Exam Dumps - Certified Ethical Hacker Exam (CEHv13)

CEH categorizes spear phishing as a highly targeted, research-driven social engineering technique that tailors the message to the victim’s role, responsibilities, and current organizational activities. When attackers reference specific internal projects, personnel names, vendor relationships, or operational details, the message appears authentic and bypasses normal suspicion. Executives are especially vulnerable because they routinely receive sensitive operational updates and work closely with vendors and partners, making them prime targets for tailored deception. CEH stresses that spear phishing is significantly more effective than generic phishing because personalization increases trust. Social media-based attempts and mass phishing lack specificity and raise suspicion. Impersonating the CEO over the phone is riskier and more detectable due to real-time human interaction. A targeted spear-phishing email referencing internal projects best aligns with CEH-described advanced social engineering strategy.

Man-in-the-Cloud (MITC) attacks target cloud synchronization services such as Dropbox, Google Drive, and OneDrive by manipulating authentication tokens instead of user passwords. CEH courseware explains that cloud storage clients rely heavily on sync tokens stored locally, which authorize access without user interaction. If an attacker replaces or injects a malicious token, they can hijack the victim’s cloud account or redirect synced data to attacker-controlled locations. In this scenario, the EC2 instance continues to operate normally, but its Dropbox client silently synchronizes data to an external account using the attacker’s token. This bypasses traditional defenses such as perimeter firewalls, credential monitoring, or user behavior analytics, because the synchronization process appears legitimate and authenticated. MITC attacks are uniquely stealthy, as they require no further compromise once the sync token is stolen or replaced. Other choices, such as Cloud Snooper or cryptojacking, do not match the described behavior. Therefore, this attack clearly aligns with a Man-in-the-Cloud attack.

This scenario clearly describes Load Balancing as a DDoS mitigation strategy, which is covered under the CEH v13 Network and Perimeter Hacking module. Load balancing distributes incoming network or application traffic across multiple servers or resources to prevent any single system from becoming overwhelmed.

According to CEH v13, modern DDoS mitigation architectures often combine hardware load balancers with cloud-based scrubbing centers or Content Delivery Networks (CDNs). These systems absorb high traffic volumes and ensure availability by spreading requests across multiple nodes. This approach aligns perfectly with the organization’s strategy of absorbing and distributing traffic rather than blocking it outright.

Other options are incorrect:

Black hole routing drops all traffic, including legitimate traffic.

Rate limiting restricts traffic volume rather than distributing it.

Sinkholing redirects traffic for analysis, not load distribution.

The CEH DDoS Mitigation Strategies section explains that load balancing distributes traffic across multiple servers or infrastructure components to prevent any single resource from being overwhelmed.

By using hardware load balancers and cloud-based traffic distribution, organizations can:

Absorb large volumes of attack traffic

Maintain service availability

Ensure legitimate users are served

Option B is correct and directly matches CEH’s definition.

Option A drops all traffic, including legitimate requests.

Option C focuses on traffic analysis rather than distribution.

Option D limits traffic rather than distributing it.

CEH strongly recommends load balancing as a core DDoS resilience mechanism.

The correct answer is A. Passive attack because the activity described involves monitoring and capturing information without altering data, system resources, or communications. In CEH-aligned information security concepts, passive attacks are defined by the attacker’s goal of eavesdropping—observing traffic to collect intelligence such as usernames/passwords, session identifiers, network patterns, or sensitive content—while making minimal changes that would trigger detection. The scenario explicitly states that the actor is “silently capturing clear-text credentials” and “analyzing unencrypted traffic,” and that “no modifications have been made to the data.” These are signature indicators of passive attacks such as packet sniffing and traffic analysis.

On an internal Wi-Fi network, passive attacks are particularly effective when encryption is weak or absent, or when users access services that transmit credentials in clear text. An attacker can capture packets and reconstruct sensitive information, especially where legacy protocols or misconfigurations exist. Because passive attackers do not need to inject or modify packets, they often avoid generating anomalies such as retransmissions, spoofed responses, or unexpected routing changes—helping them remain undetected, consistent with the prompt.

Why the other options do not fit: Distribution attack is not the standard classification for this behavior and does not specifically describe silent observation of traffic. Close-in attack refers to attacks that depend on physical proximity (e.g., shoulder surfing, physical tapping, local interception near the target). While Wi-Fi sniffing can require proximity, the defining characteristic in the question is the non-invasive observation with no data modification—i.e., passive attack. Insider attack relates to the attacker’s identity/role (a trusted internal person), which is not established here; the scenario only describes behavior, not who the actor is.

Therefore, the described credential capture and traffic analysis without modification most clearly indicates a passive attack.

Sherlock is the correct choice because it is specifically designed for OSINT-style username enumeration across a very large number of websites. In CEH-aligned reconnaissance, one common task is “alias correlation,” where the tester checks whether the same username exists across multiple social networks, forums, and content platforms. Sherlock automates this by querying many supported sites and reporting where an account with a given username appears to exist. This directly matches the question’s requirement: “cross-platform username correlation by scanning hundreds of social networking sites.”

The prompt also specifies what the tool does not do: it “does not natively support geolocation tracking or visualizing identity relationships.” That limitation aligns with Sherlock’s role as a focused username discovery tool rather than an analysis platform. By comparison, Creepy is associated with geolocation-focused OSINT, particularly collecting and correlating location metadata from social media posts and images, which is the opposite of the stated limitation. Maltego is well known for relationship analysis and link visualization, allowing investigators to build graphs of people, emails, domains, and social entities—again contradicting the “does not visualize identity relationships” constraint. Social Searcher is more oriented toward searching and monitoring public social media content and mentions, not primarily high-volume username existence checking across hundreds of sites in the same manner.

In CEH reconnaissance workflows, Sherlock fits early-stage enumeration to expand a target’s footprint, after which analysts may pivot into tools like Maltego for link analysis or geolocation tools when location exposure is part of the assessment.

CEH defines spyware as malware designed to covertly observe user behavior and transmit sensitive information to attackers without the victim’s knowledge. Spyware commonly records keystrokes, browser activity, form submissions, application usage, and other personally identifiable information. CEH highlights that spyware often operates silently and may disguise itself as legitimate software, making detection difficult. Unlike rootkits—which hide processes and files—or worms that self-replicate, spyware focuses exclusively on monitoring and data exfiltration. It is frequently installed through phishing, drive-by downloads, browser vulnerabilities, or malicious installers. Spyware can serve as a stepping stone for further system compromise by providing attackers with credentials for privilege escalation, lateral movement, or financial theft. CEH emphasizes the need for endpoint hardening, updated anti-malware engines, and behavioral analysis tools to detect such stealthy monitoring programs.

The CEH SQL Injection module defines stacked (piggybacked) queries as attacks where an attacker appends an additional SQL statement using a statement delimiter such as a semicolon.

In this scenario, the attacker executed a second query (DROP TABLE users) after the original query, resulting in destructive behavior.

Option B is correct.

Option A retrieves data, not execute destructive commands.

Option C infers logic outcomes.

Option D relies on error messages for data extraction.

CEH classifies stacked queries as high-impact SQL injection attacks.