300-220 Exam Dumps : Conducting Threat Hunting and Defending using Cisco Technologies for Cybersecurity 300-220 CBRTHD

The correct answer is SQL injection. The decoded HTTP request shown in the exhibit contains multiple unmistakable indicators of a SQL injection attack, including the use of SQL keywords and functions such as SELECT, CASE, SUBSTRING, ASCII, BIN, and conditional SLEEP() statements. These elements are characteristic of time-based blind SQL injection, a technique attackers use to extract database information when direct query results are not visible.

From a professional cybersecurity perspective, the presence of expressions like:

SELECT (CASE WHEN … THEN SLEEP(x))

SUBSTRING(password,1,1)

ASCII() and binary conversions

indicates that the attacker is probing the backend database character by character and using response timing to infer whether conditions are true or false. This is a well-known exploitation method used when error messages or query output are suppressed by the application.

The use of Base64 encoding does not represent the attack itself but rather an obfuscation technique to evade basic web application firewall (WAF) signatures and logging visibility. Encoding payloads allows attackers to bypass simple pattern-matching defenses, but once decoded, the underlying SQL injection becomes evident.

Option A (Unicode encoding) is incorrect because Unicode is commonly used for evasion, not exploitation. Option C (directory traversal) typically involves sequences like ../ to access filesystem paths, which are not present. Option D (XSS) targets client-side script execution and would include JavaScript payloads rather than database-focused logic.

According to the MITRE ATT&CK framework, this activity maps to Initial Access – Exploit Public-Facing Application (T1190). SQL injection remains one of the most exploited vulnerabilities in public-facing applications due to poor input validation and insecure coding practices.

For threat hunters and defenders, this scenario reinforces the importance of deep payload inspection, decoding obfuscated requests, monitoring for anomalous database query behavior, and enforcing secure development practices such as parameterized queries and input sanitization. SQL injection continues to be a high-impact, real-world attack vector despite being well understood, making it a critical focus area in web application threat hunting.

The correct next action is tosubmit the file for sandboxing. In professional security operations and threat hunting workflows, sandboxing is the most appropriate step when a file originates from an untrusted source and hash-based reputation checks return anunknownresult. An unknown hash means the file has not yet been classified as benign or malicious by threat intelligence databases, which is common with newly created malware or targeted attacks.

Sandboxing allows the security team to performdynamic analysisby executing the file in an isolated, controlled environment. This process observes runtime behaviors such as process creation, registry modification, network communications, command-and-control callbacks, file system changes, and exploit attempts. These behaviors provide high-fidelity indicators that static analysis or hash lookups cannot reveal.

Option B, reviewing the directory path, is useful for contextual awareness but does not determine whether the file is malicious. Option C, running a full malware scan, is premature; modern malware often evades signature-based scans, especially when the file is previously unknown. Option D, investigating the reputation of the website, is a supporting activity but does not assess the actual behavior or payload of the downloaded file.

From a threat hunting and incident response standpoint, sandboxing bridges the gap betweendetection and confirmation. If the sandbox analysis confirms malicious behavior, the team can escalate to containment actions such as isolating the endpoint, blocking hashes and domains, and performing scope analysis to identify other affected systems. Additionally, sandbox results can be used to create new SIEM detections and EDR behavioral rules, strengthening future defenses.

This approach aligns with professional best practices:unknown file + untrusted source = dynamic analysis first. It ensures accurate classification while minimizing unnecessary disruption to the user or environment.

The correct answer isThey produce false positives and false negatives. Automated dynamic malware analysis tools, such as sandboxes, execute suspicious files in isolated environments to observe runtime behavior. While these tools are extremely valuable in modern SOCs, they are not infallible and have well-known limitations.

False positives occur when benign software exhibits behaviors that resemble malicious activity, such as creating registry keys, spawning child processes, or making network connections. Conversely, false negatives occur when malware intentionallyevades detectionby altering its behavior. Many modern malware samples include sandbox evasion techniques such as checking for virtualized environments, delaying execution, requiring user interaction, or disabling malicious functionality when analysis artifacts are detected.

Option A is incorrect because dynamic analysis can, in fact, uncover runtime vulnerabilities and exploit behavior. Option C is misleading; while tooling coverage varies, language support is not the primary limitation. Option D refers tomanual analysis, whereas the question explicitly addresses automated tools.

From a threat hunting and malware analysis standpoint, professionals understand that sandbox results must becorrelated with other telemetry, including endpoint behavior, network traffic, and threat intelligence. Automated tools are best used as part of a layered analysis strategy, not as a single source of truth.

This limitation reinforces a core security principle:automation accelerates detection, but human judgment validates impact. Skilled analysts interpret sandbox results, investigate anomalies, and determine real-world risk. Understanding the strengths and weaknesses of dynamic analysis tools allows SOC teams to avoid overreliance on automation while still benefiting from its speed and scale.