BSI Generative AI Risks :$-$

Unwanted Outputs, Literal Memory Bias: Bias, discriminator, Memory bias.

Lack Of Quality, Factuality, Hallucinating :$-$ Quality, factuality $,$ hallucination, inaccuracies

Lack of timeliness :$-$ Timeliness, outdated, irrelevant

Lack of reproducibility and explain ability :$-$ Reproducibility, explain ability, Transparency

Lack Of security Of generated code :$-$ Security, vulnerabilities, breaches

Incorrect response to specific inputs : incorrect response, inappropriate outputs

Automation Bias:$-$ Automation, over$-$reliance, huma error

susceptibility to interpreting text as instruction :$-$ Misinterpretation, unintended actions

Lack of confidentiality of input data; confidentiality, data leaks $,$ misuse.

Self$-$Reinforcing impacts and model collapse : self$-$reinforcing, model collapse, degradation

Dependence on the model Developer$/$operator :$-$ Dependence, limited adaptability, reliance

Misinformation $($fake news$)$ : misinformation $,$ fake news, misleading

Social Engineering :$-$ social engineering, manipulation, deception

Re$-$identification of individuals from anonymous data:$-$ Re$-$identification, privacy, anonymized

data

Knowledge Gathering And Processing in the context of surveillance :$-$ Surveillance, privacy concerns

Malware creation and improvement : Malware, enhancement

malware creation and improvement :$-$ distribution, deployment

RCE $($Remote Code Executions$)$ Attacks :$-$ RCE, Code Execution, vulnerabilities

Attackers can Reconstruct a model's training data: Data reconstruction, sensitive information

Model Subversion Attacks :$-$ model subversion, unintended outputs

member reasoning Attacks :$-$ Reasoning attacks, inference

Homograph Attacks :$-$ Homograph $,$ deceptive

Data Poisoning Attacks : Data poisoning $,$ Corrupted data

Model Poisoning Attacks :$-$ model poisoning, behavior manipulation

leaning Transfer attacks : Learning transfer, intellectual property

NIST AI Risks :$-$

Accuracy :$-$ Accurate $,$ correct outputs

Reliability and Robustness :$-$ Reliable, robust, fault$-$tolerant

Accountability : Accountability, responsible, oversight

Exploitability and interpretability :$-$ Explainable, interpretable, understandable

Fairness and Bias :$-$ Fairness, unbiases, equality

privacy :$-$ Privacy, data protection, confidentiality

Security :$-$ Secure, safe, protected

safety :$-$ safe, non$-$harmful, risk$-$free

Resilience : Resilient, withstand, durable

Transparency :$-$ Transparent, open, clear

Immitigability mitigable :$-$ controllable, preventable

Ethical considerations:$-$ Ethical, moral, principled.

Explanation:

refer to the above solution $.$

Step $2$

Successful User Prompts for identifying Risks:$-$

$1)$Bias and Discriminations :$-$

identify instances of bias in the model outputs.

check for discriminatory patterns in the training data.

$2)$ Hallucination and inaccuracies :$-$

Validate the factual accuracy of the generated

identify instances where the model hallucinates information.

$3)$ outdated information:$-$

check if the data sources are current and relevant.

identify outputs that may be based on outdated information.

$4)$ Reproducibility and explain ability :$-$

Ensure the model's decisions can be reproduced and explained.

Identify areas where the model's decision$-$making is opaque.

$5)$Security vulnerabilities :$-$

"check for security vulnerabilities in the generated code.

identify any potential breaches in data handling.

$6)$ incorrect responses ;$-$

"test the model for incorrect or inappropriate responses to specific inputs.

identify limitations in understanding certain types of inputs.

$7)$ Automation Bias:

Assess the model's impact on human decision$-$making processes.

Identify areas where over$-$reliance on automation might lead to errors.

$8)$ Misinterpretation of text:

check for instances where the model misinterprets text as instructions.

Identify unintended actions triggered by ambiguous inputs.

Explanation:

refer to the above solution $.$

Step $3$

identifying risks at Deferent lifecycle phases:

$1)$ Data collection and preparation :$-$

Bias, privacy and data poisoning risks should be identified.

Prompts "assess the training data for biases, $"$ check for potential data Leask

during collection.

$2)$ Model training and development :$-$

Accuracy, reliability, robustness, and explain ability should be ensured.

Prompts. Ensure the model training process is transparent $,$ Validated the model's

accuracy and robustness.

$3)$Deployment and monitoring :$-$

Security safety, and real$-$time reliability must be monitored.

Prompts. Monitor the deployed model for security vulnerabilities, "check the

model's performance in real$-$time environments".

$4)$ Maintenance and updates:

continuously ensure timelines, adaptability, and mitigation of new risks.

Prompts. "update the model to ensure data relevancy", check for new for each risk find successful promts for the user $,$ try to make a categorization of keywords and promts for each paper according to the lifecycle phase