CEH v13 Changed 312-50v13 Questions

The Common Vulnerability Scoring System (CVSS) is a method used to supply a qualitative measure of severity for a vulnerability. CVSS consists of three metric groups: Base, Temporal, and Environmental. The Base metrics produce a score ranging from 0 to 10, which can then be modified by scoring the Temporal and Environmental metrics. A vector string represents the values of all the metrics as a block of text1

The Base metrics measure the intrinsic characteristics of a vulnerability, such as the attack vector, the attack complexity, the required privileges, the user interaction, the scope, and the impact on confidentiality, integrity, and availability. The Base score reflects the severity of a vulnerability assuming that there is no temporal information or context available1

The Temporal metrics measure the characteristics of a vulnerability that change over time, such as the exploit code maturity, the remediation level, and the report confidence. The Temporal score reflects the current state of a vulnerability and its likelihood of being exploited1

The Environmental metrics measure the characteristics of a vulnerability that depend on a specific implementation or environment, such as the security requirements, the modified base metrics, and the collateral damage potential. The Environmental score reflects the impact of a vulnerability on a particular organization or system1

In this scenario, the vulnerability has a Base score of 7, a Temporal score of 8, and an Environmental score of 5. This means that:

The vulnerability has a high severity based on its intrinsic characteristics, such as the attack vector, the attack complexity, the required privileges, the user interaction, the scope, and the impact on confidentiality, integrity, and availability. A Base score of 7 corresponds to a high severity rating according to the CVSS v3.0 specification1

The vulnerability has an increasing likelihood of exploitability over time based on its current state, such as the exploit code maturity, the remediation level, and the report confidence. A Temporal score of 8 is higher than the Base score of 7, which indicates that the vulnerability is more likely to be exploited as time passes1

The vulnerability has a medium impact on the specific environment or implementation based on the security requirements, the modified base metrics, and the collateral damage potential. An Environmental score of 5 is lower than the Base score of 7, which indicates that the vulnerability is less impactful in the particular context of the organization or system1

Therefore, the statement that best describes this scenario is: The vulnerability has an overall high severity, the likelihood of exploitability is increasing over time, and it has a medium impact in their specific environment.

The base metric represents the inherent qualities of a vulnerability, according to the Common Vulnerability Scoring System (CVSS). CVSS is a framework that numerically characterizes the severity of software vulnerabilities between the range of 0-10. CVSS consists of three metric groups: Base, Temporal, and Environmental. The base metric group captures the characteristics of a vulnerability that are constant over time and across user environments. The base metric group consists of six sub-metrics: Attack Vector, Attack Complexity, Privileges Required, User Interaction, Scope, and Impact. The impact sub-metric further consists of three sub-metrics: Confidentiality, Integrity, and Availability. The base metric group produces a score ranging from 0 to 10, which reflects the intrinsic and fundamental properties of a vulnerability12.

The other options are not correct for the following reasons:

A. Temporal metric represents the inherent qualities of a vulnerability: This option is incorrect because the temporal metric group captures the characteristics of a vulnerability that change over time due to events external to the vulnerability. The temporal metric group consists of three sub-metrics: Exploit Code Maturity, Remediation Level, and Report Confidence. The temporal metric group modifies the base score to reflect the current state of the vulnerability, such as the availability of exploit code, the existence of patches or workarounds, and the degree of verification of the vulnerability report12.

C. Environmental metric involves the features that change during the lifetime of the vulnerability: This option is incorrect because the environmental metric group captures the characteristics of a vulnerability that are relevant and unique to a user’s environment. The environmental metric group consists of three sub-metrics: Modified Attack Vector, Modified Attack Complexity, and Modified Privileges Required. The environmental metric group also allows the user to assign importance values to the impact sub-metrics: Confidentiality Requirement, Integrity Requirement, and Availability Requirement. The environmental metric group modifies the base and temporal scores to reflect the impact of the vulnerability on the user’s specific environment, such as the network configuration, the security objectives, and the asset value12.

D. Temporal metric involves measuring vulnerabilities based on a specific environment or implementation: This option is incorrect because the temporal metric group does not involve measuring vulnerabilities based on a specific environment or implementation, but rather on the factors that change over time due to events external to the vulnerability. The environmental metric group, not the temporal metric group, involves measuring vulnerabilities based on a specific environment or implementation, as explained in option C.

In CEH v13 Module 02: Footprinting and Reconnaissance, Dark Web Footprinting is discussed as an advanced form of reconnaissance where attackers access hidden services and data using anonymity networks such as Tor (The Onion Router), I2P, or Freenet. These networks enable access to the deep web and dark web, where unindexed, and often illicit, content resides.

Key points relevant to this scenario:

The attacker encrypted browsing traffic and navigated anonymously, which strongly implies the use of tools like Tor or VPNs to mask identity.

The attacker used specialized tools/search engines like:

Torch

Ahmia

DarkSearch

Candle

The goal was to find sensitive or hidden information in government or federal systems — a common dark web footprinting objective.

The final step involved an attack that left no trace, which aligns with using the dark web for anonymity and obfuscation.

Option Analysis:

A. Dark web footprinting

Correct. This matches the behavior described: encrypted, anonymous access to sensitive information through dark web tools.

B. VoIP footprinting ❌

Incorrect. VoIP footprinting relates to identifying vulnerabilities or metadata in Voice over IP systems, not anonymous browsing or dark web activities.

C. VPN footprinting ❌

Incorrect. While VPNs may be used as part of anonymity, VPN footprinting refers to identifying systems using VPNs — not the act of gathering data anonymously.

D. Website footprinting ❌

Incorrect. Website footprinting involves gathering information from public-facing websites, like WHOIS data, robots.txt, and HTML metadata — not hidden dark web content.

Reference from CEH v13 Study Guide and Courseware:

Module 02 – Footprinting and Reconnaissance, Section: Footprinting through Dark Web and Deep Web

CEH v13 iLabs: Using Tor and Dark Web Search Engines for Reconnaissance

CEH Engage – Phase 1 (Reconnaissance): Dark Web Intelligence Gathering

IoT devices often suffer from weak or missing encryption protocols, creating opportunities for attackers to intercept sensitive information. CEH courseware highlights that when device-to-app communication occurs in plaintext, a man-in-the-middle attack becomes one of the most effective exploitation methods. By positioning themselves between the device and the mobile application, the attacker can observe all transmitted data, including configuration updates, authentication tokens, and personal information. Tools such as Wireshark, mitmproxy, and specialized IoT interception frameworks allow testers to capture packets, decode protocols, and reconstruct application logic. The CEH curriculum stresses the importance of evaluating IoT device communication channels, because many rely on unsecured HTTP or proprietary plaintext protocols. Brute-force attempts, SQL injection, or dictionary attacks do not directly target the device–app communication pathway and would not exploit the fundamental weakness present here: lack of encryption. A MitM attack directly takes advantage of this flaw and provides comprehensive visibility into all transmitted data, making it the most effective and CEH-aligned method of exploitation.