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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Summarize basics of cryptographic concepts - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks |
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation |
| Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
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NEW QUESTION # 110
A security analyst notices that regardless of user-submitted prompts, an AI model always returns unsanitized responses. These responses are then passed to multiple plug-ins. The analyst is concerned with the potential security implications.
Which of the following Open Worldwide Application Security Project (OWASP) categories addresses this vulnerability?
Answer: A
Explanation:
Basic Concept: OWASP has published the Top 10 vulnerabilities for Large Language Model Applications, each addressing a distinct category of LLM security risk. Understanding which OWASP category maps to specific LLM vulnerability scenarios is a key competency in the CompTIA SecAI+ Study Guide under securing AI systems.
Why D is Correct: Improper output handling (OWASP LLM02) occurs when an application passes LLM- generated outputs to downstream systems such as plug-ins, web browsers, or databases without proper validation, sanitization, or encoding. This can enable XSS, SQL injection, remote code execution, or other injection attacks against plug-ins and downstream systems. The scenario exactly matches this: unsanitized AI responses are automatically passed to multiple plug-ins, which could execute malicious content in the model ' s output.
Why A is Wrong: Misinformation refers to the AI generating false or misleading content that users might believe. It is a content accuracy concern related to hallucinations and false information propagation, not a vulnerability describing how model outputs are handled by downstream systems.
Why B is Wrong: Prompt injection involves crafting inputs to manipulate model behavior and override instructions. While it can be a contributing cause of unsafe outputs, the vulnerability described - passing unsanitized outputs to plug-ins - is specifically the output handling failure, not the injection mechanism itself.
Why C is Wrong: Unbounded consumption (OWASP LLM10) refers to resource exhaustion attacks including denial-of-wallet and denial-of-service through excessive token consumption. It addresses resource management vulnerabilities, not the security implications of passing model outputs to downstream systems.
NEW QUESTION # 111
Global IPs attempt logins against thousands of accounts with known breached credentials. What attack is occurring?
Answer: C
Explanation:
Credential stuffing uses leaked username-password pairs.
NEW QUESTION # 112
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: A,B
Explanation:
Basic Concept: Prompting techniques for LLMs include various approaches to guide model behavior.
Providing examples within prompts is a powerful technique that leverages the model ' s in-context learning capability to guide response format and quality. CompTIA SecAI+ Study Guide covers prompting techniques under basic AI concepts.
Why E is Correct: One-shot prompting involves providing exactly one example within a prompt to demonstrate to the model the desired input-output format or response style. This single example guides the model ' s understanding of the task without requiring extensive fine-tuning. It is a well-established prompting technique that uses examples to inform model behavior.
Why F is Correct: Multi-shot prompting (also called few-shot prompting) involves providing multiple examples within a prompt to further clarify the desired output pattern. Multiple examples help the model identify consistent patterns and produce more accurate, consistent responses. Both one-shot and multi-shot are specifically defined by their use of examples in prompts.
Why A is Wrong: A user prompt is the input message submitted by a user to the AI system. It is the general term for any user input, not a specific technique that describes the practice of providing examples.
Why B is Wrong: A system prompt sets the model ' s behavior, persona, and constraints at the session level.
While a system prompt could contain examples, the term specifically refers to the system-level instruction context, not the technique of example provision.
Why C is Wrong: A prompt template is a reusable structured format with placeholders for variable inputs. It standardizes prompt structure but is not defined by the practice of including examples.
Why D is Wrong: Quantization is a model compression technique that reduces model size by representing weights with lower precision numbers. It is a model optimization technique completely unrelated to prompting practices.
NEW QUESTION # 113
SIMULATION
Instructions
Part1
Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part2
Use the drop-down menu to select the most appropriate technique to apply to the modified data.
It at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption.
The engineer must implement data security.
Answer:
Explanation:
Explanation:
Part 1 (Protocols/Ciphers):
API Gateway: TLS 1.2
* Database: AES-512
* AI Model: gRPC
Part 2 (Techniques):
1. {pin:"999-99-9999", name:"john doe"} → {pin:"999-99-9999", name:"john doe", sensitivity:"SECRET"} → Classification
2. {ip_addr:"1.2.3.4", cookie:"aK3idkd==", name:"John Doe" uid="1111"} → {cookie:"aK3idkd==", uid="1111"} → De-identification
3. {pin:"999-99-9999", name:"john doe"} → {pin:"999-99-XXXX", name:"john doe"} → Masking
4. {card_number:"1111222233334444", name:"john doe", user_id:"1"} →
{card_number:"0x019238829", name:"john doe", user_id:"1"} → Tokenization
5. {name:"john doe", patient_id:"10000", dob:"1980-Jan-05"} → {patient_id:"10000", dob:"1980- Jan-05"} → Anonymization Part 1:
* TLS 1.2 secures client-to-gateway communications.
* AES-512 provides strong encryption for data at rest in the database.
* gRPC ensures efficient, secure communication between services (AI model).
Part 2:
* Classification tags sensitive data for handling.
* De-identification strips direct identifiers.
* Masking obscures part of sensitive values while keeping format.
* Tokenization replaces sensitive data with a reversible placeholder.
* Anonymization removes identifying attributes, making re-identification impossible.
NEW QUESTION # 114
As a compliance requirement, a large language model (LLM) application requires setting up guardrails.
Which of the following resources is most appropriate to use?
Answer: A
Explanation:
Basic Concept: When implementing guardrails for compliance purposes, organizations need a recognized framework or standard that provides authoritative guidance on what guardrails should address and how to implement them. Compliance guardrails require industry-recognized standards as their basis. CompTIA SecAI+ Study Guide identifies OWASP as the primary reference for LLM application security controls including guardrails.
Why B is Correct: OWASP provides the OWASP Top 10 for Large Language Model Applications, which is a recognized industry resource defining the most critical vulnerabilities in LLM applications and the guardrails needed to mitigate them. Using OWASP as the reference for compliance-required guardrails provides a defensible, industry-standard basis for the security controls implemented, satisfying compliance requirements with authoritative guidance on what guardrails should prevent and how they should function.
Why A is Wrong: RAG is an AI architecture that enhances LLM responses with retrieved external context. It is a capability enhancement technique, not a framework for defining or implementing security guardrails for compliance purposes.
Why C is Wrong: LLM libraries are software development toolkits that provide functions for working with language models. While they may include built-in guardrail features, they are implementation tools, not the governance resource or standard that compliance guardrail requirements should be based upon.
Why D is Wrong: A SIEM is a security monitoring and alerting platform that aggregates and analyzes log data. It is a detection and monitoring tool, not a framework that defines what guardrails are required for LLM application compliance.
NEW QUESTION # 115
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