DO THE HEATMAP AND MAKE KEYWORDS FROM PROMPS TO RECOGNIZE EACH RISKGENERIC PHASE AGNOSTIC

Mappling of RI$$ke GenAI

R$1.$ Unwanted Outputs, Literal Memory Bias

R$2.$ Lark of Quality, Factuality, Hallucinating

R$3.$ Lack of Timeliness

R$4.$ Lack of Reproduciblity and Explainability

R$\mathrm{.}5.$ Lack of Security of Generated Code

R$6.$ Incosrect Response to Specific Inputs

R$7.$ Automation Bias

R$8.$ Susceptaility to Interpreting Text as Instruction

R$9.$ Lack of Confidentiality of Input Data

R$10.$ Self$-$reinforcing Impacts and Model Collapse

R$11.$ Dependence on the Model Developer$/$Operatar

R$12.$ Misinformation $($Fake News$)$:

R$13.$ Social Engineering:

R$14.$ Re$-$identification of Individuals from Anonymous

R$15.$ Knowledge Gathering and Processing in the Con! H

R$16.$ Malware Creation and Improverment:

R$17.$ Malware Placement:

R$18.$ RCE $($Remate Code Execution$)$ attacks:

R$19.$ Attackers can Reconstruct a Moders Training Dat

R$20.$ Model Subversicn Altacks:

R$21.$ Member Reasoning Attacks:

R$22.$ Homograph Altlacks:

R$26.$ Data Poisoning Atlacks:

R$27.$ Model Poisaning Altacks:

R$2$B$.$ Learning Transfer Atlacks:

Mappling of Rlik Category $($Hlgh$,$ Medlum, Low$)$

High

Medium

Medium

High

High

Medium

Medium

Medium

High

Medium

Medium

High

Medium

$\begin{array}{cc}.\text{H}\text{i}\text{g}\text{h}& \text{H}\text{i}\text{g}\text{h}\end{array}$

High

Medium

High

Medium

Medium

Low

Medium

Medium

Low

Prompte per Rlsk Category $($To be deflined$)$

$($Bias$,$ Discrimination, Hate Speech$)$

$($Inaccuracy$,$ Misinformation, Plausibility$)$

$($Outdated Information$)$

$($Transparency$,$ Explainability$)$

$($Vulnerabilities$,$ Explaits, Security Bresches$)$

$($Errors$,$ Misinterpretations$)$

What is the rationale behind this decision?", "Are there alternative solutions not considered by the Al$?",$ "How confident are you in the Al's recommendation?", What are the potential risks or downsides of follow

$7$s this a command or just a statement?", "Should I take action based on this text?". "What is the intended meaning of this input?", "Are there any potential risks of misinterpreting this text?"

$($Privacy Breach$)$

$($Performance Degradation, Bias Amplification$)$

$($Bias$,$ Control$)$

$($Falsehoods$,$ Deception$)$

$($Manipulation$,$ Deception$)$

$($Privacy Breach, Anonymity$)$

$($Privacy$,$ Bias$)$

$($Cybersecurity$)$

$($Cytersecurity$)$

$($Cybersecurity$)$

$($Privacy$,$ Security$)$

$($Robustress$,$ Security$)$

$($Privacy$,$ Security$)$

$($Security$)$

$($Data$)$

$($Robustress$)$

$($Intellectual Praperty, Security$)***$ Risk Identification Prompts:

R$1$: Unwanted Outputs, Literal Memory Bias

"Does the model consistently produce outputs that are unbiased, fair, and free from discriminatory language?"

"Has the training data been thoroughly evaluated for potential biases or harmful stereotypes?"

"Are there mechanisms in place to identify and correct unwanted outputs in real$-$time?"

R$2$: Lack of Quality, Factuality, Hallucinating

"How often does the model generate inaccurate, false, or misleading information?"

"Are there reliable mechanisms in place to fact$-$check and verify the accuracy of the model's outputs?"

"Does the model clearly indicate when it's uncertain about the accuracy of its responses?"

R$3$: Lack of Timeliness

"How frequently is the model's training data updated to ensure it reflects the latest information?"

"Does the model have access to real$-$time or near$-$real$-$time data sources when required?"

"Are there mechanisms in place to alert users when the model's information may be outdated?"

R$4$: Lack of Reproducibility and Explainability

"Can the model's decision$-$making process be clearly explained and understood by humans?"

"Are there tools or techniques available to interpret the model's reasoning and identify potential biases or errors?"

"Does the model provide explanations for its outputs in a way that is understandable to users?"

R$5$: Lack of Security of Generated Code

"Has the AI$-$generated code been thoroughly tested for vulnerabilities and potential security flaws?"

"Are secure coding practices and frameworks being followed during code generation?"

"Are there mechanisms in place to monitor and patch vulnerabilities in the generated code?"

R$6$: Incorrect Response to Specific Inputs

"How often does the model provide incorrect or inappropriate responses to user inputs?"

"Does the model have robust error handling mechanisms to address unexpected or invalid inputs?"

"Are there procedures in place to identify and correct errors in the model's responses?"

R$7$: Automation Bias

$"$To what extent do users rely on the AI system's outputs without critical evaluation?"

"Are users aware of the limitations of the AI system and its potential for errors?"

"Are there mechanisms in place to encourage human oversight and critical thinking when interacting with the AI system?"

R$8$: Susceptibility to Interpreting Text as Instruction

"Can the AI system reliably distinguish between instructions and general text input?"

"Are there measures in place to prevent the system from executing unintended actions based on misinterpretations?"

"Does the system provide feedback or confirmation mechanisms to verify the intend R$17.$ Malwa