CAS-005 Exam Dumps : CompTIA SecurityX Certification Exam

The provided code snippet shows a script that captures the user's cookies and sends them to a remote server. This type of attack is characteristic of Cross-Site Scripting (XSS), specifically stored XSS, where the malicious script is stored on the target server (e.g., in a database) and executed in the context of users who visit the infected web page.

A. XSRF (Cross-Site Request Forgery) attack: This involves tricking the user into performing actions on a different site without their knowledge but does not involve stealing cookies via script injection.

B. Command injection: This involves executing arbitrary commands on the host operating system, which is not relevant to the given JavaScript code.

C. Stored XSS: The provided code snippet matches the pattern of a stored XSS attack, where the script is injected into a web page, and when users visit the page, the script executes and sends theuser's cookies to the attacker's server.

D. SQL injection: This involves injecting malicious SQL queries into the database and is unrelated to the given JavaScript code.

The Common Vulnerability Scoring System (CVSS) v3.1 uses three metric groups to calculate overall scores:Base,Temporal, andEnvironmental.

Base (E):Mandatory metrics assessing exploitability (e.g., attack vector) and impact (confidentiality, integrity, availability).

Temporal (A):Optional metrics reflecting the current state of the vulnerability (e.g., exploit availability, remediation level).

Environmental (F):Optional metrics tailoring the score to the organization’s context (e.g., security requirements).

B, C, D (Availability, Integrity, Confidentiality):These are subcomponents of the Base Impact metrics, not standalone groups.

G (Impact):A categorywithin Base, not a group.

H (Attack vector):A single Base metric, not a group.

The organization is most concerned about Generative AI improving phishing and social engineering attacks. Tools like ChatGPT can generate highly convincing phishing emails, fake websites, and human-like interactions that bypass traditional detection methods. Employees who were trained to spot poor grammar or obvious scams may now struggle to detect AI-crafted exploits.

Option A relates to compliance but not AI-driven threats. Option B (overreliance on AI bots) is operational risk, not phishing. Option D (differential analysis) applies to AI privacy issues, not phishing.

CAS-005 emphasizes adapting training to emerging threats, including AI-enabled social engineering. This ensures users remain resilient against modern attacks, making C the correct answer.