Exin AISP Dumps

Exin AISP Questions Answers

Association of Information Security Professional (AiSP)
  • 99 Questions & Answers
  • Update Date : October 22, 2026

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Exin AISP Sample Questions

Question # 1

2.3 Runtime conventional security threats · Intermediate An employee deletes a confidential document from the intranet. Weeks later, the RAG assistant still answers from it, because its chunks and embeddings were never removed from the vector store. Which threat is this?

A. Augmentation data leak through incomplete deletion propagation 
B. Model inversion 
C. Training data poisoning 
D. Output containing conventional injection 



Question # 2

2.3 Runtime conventional security threats · Foundational  An application writes every user prompt to plaintext debug logs readable by a large engineering group. Prompts often contain customer health details. Which threat is this?

A. Disclosure in model output 
B. Input data leak 
C. Augmentation data manipulation 
D. Model inversion 



Question # 3

2.3 Runtime conventional security threats · Advanced A chatbot renders Markdown. An injected instruction makes it output an image link whose URL contains the user's conversation data, so the browser sends the data to an attacker's server. Which control most directly stops the leak path?

A. Rate limiting 
B. Stronger model alignment 
C. Minimizing training data 
D. Sanitizing or encoding model output before rendering, such as blocking or allowlisting external image URLs



Question # 4

2.3 Runtime conventional security threats · Intermediate An analytics assistant generates SQL that the application executes directly. A user persuades it to produce a statement that drops a table. Which threat and control fit best?

A. Data poisoning; retraining 
B. Direct runtime model leak; encryption 
C. Output containing conventional injection; treat output as untrusted by validating or parameterizing it and running it with least-privilege database rights
D. Membership inference; obscure confidence 



Question # 5

2.3 Runtime conventional security threats · Intermediate An attacker with access to an inference host patches the loaded recommendation model's parameters so a competitor's products are never recommended. Which threat is this? 

A. Direct runtime model poisoning 
B. Data poisoning 
C. Supply-chain model poisoning 
D. Evasion 



Question # 6

2.3 Runtime conventional security threats · Advanced A company ships a proprietary model inside an app on customer devices. Attackers extract the weights from device memory. Which threat and control pair fits best?

A. Model exfiltration through queries; rate limiting 
B. Direct runtime model leak; runtime model confidentiality, such as trusted execution environments or model obfuscation
C. Development-time model leak; encrypting the training data 
D. Evasion; adversarial training 



Question # 7

2.3 Runtime conventional security threats · Intermediate Which control most directly counters direct runtime model poisoning?

A. Input segregation 
B. Obscure confidence
C. Runtime model integrity: verify signatures or hashes and restrict write access to deployed model artifacts
D. Data minimization 



Question # 8

2.3 Runtime conventional security threats · Intermediate An inference server loads its model from a file that the web application's service account can write to. The web application has a remote code execution flaw. What is the main AI-specific concern?

A. Rate limits will be exceeded 
B. Membership inference becomes possible 
C. Prompt injection becomes possible 
D. Aconventional compromise lets the attacker swap or alter the deployed model, so runtime model integrity controls are needed



Question # 9

2.3 Runtime conventional security threats · Foundational Why does the AI Exchange include generic runtime security threats, such as server compromise, in AI security?

A. AI systems run on conventional IT, so a break-in can expose or alter AI-specific assets, and conventional controls must protect them
B. AI systems are immune to conventional attacks 
C. Conventional threats only affect training
D. Model alignment covers conventional threats 



Question # 10

2.2 Development-time threats · Advanced The weights of a proprietary credit model, trained on confidential data, sit in a storage bucket readable by every employee. Beyond loss of intellectual property, what risk does this create?

A. Only reputational damage 
B. Insiders or intruders can mount perfect-knowledge evasion and model inversion attacks offline 
C. The model becomes poisoned 
D. None; weight files are unreadable binaries 



Question # 11

2.2 Development-time threats · Intermediate A data scientist copies a production customer dataset to a personal laptop for experiments. The laptop is stolen. Which threat has materialized?

A. Model inversion 
B. Augmentation data leak 
C. Development-time data leak 
D. Direct development-time model leak 



Question # 12

2.2 Development-time threats · Foundational Atraining configuration file containing hyperparameters and cloud storage credentials is pushed to a public code repository. Which threat is this?

A. Development-time data leak 
B. Direct runtime model leak 
C. Input data leak 
D. Source code/configuration leak 



Question # 13

2.2 Development-time threats · Advanced Before integrating a third-party open-weights model, what is the best set of measures?

A. Verify provenance and integrity (signatures or hashes from a trusted source), prefer safe serialization formats, and test behavior before deployment
B. Use download count and popularity as the main trust signal 
C. Fine-tune immediately, since fine-tuning reliably removes backdoors 
D. Rely solely on runtime output scanning 



Question # 14

2.2 Development-time threats · Intermediate Ateam downloads a pre-trained model from a public model hub and fine-tunes it. Later, they discover the original model contained a backdoor. Which threat is this?

A. Direct development-time model poisoning 
B. Evasion 
C. Supply-chain model poisoning 
D. Development-time data leak 



Question # 15

2.2 Development-time threats · Intermediate Which control set best prevents direct development-time model poisoning?

A. Obscure confidence and rate limiting 
B. Development environment security: access control on pipelines and artifacts, segregation, and integrity checks on code and weights
C. Input segregation 
D. Output encoding 




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