Create MarkusHupfauer_Unwanted-AI-Actions (#315)

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+## Unwanted AI Actions by General Purpose LLMs
+
+**Author(s):**
+
+[Markus Hupfauer](mailto:[email protected])
+
+### Description
+
+Unwanted AI Actions occur when AI systems employing general-purpose large language models (LLMs) are not sufficiently tailored to their specific application contexts. 
+Inadequately configured system prompts or insufficient fine-tuning can result in AI models performing illegal or undesirable actions within their operational environments, leading to potential legal liabilities and reputational damage.
+
+### Common Examples of Risk
+
+1. A health insurance AI unlawfully provides patient healthcare advice, overstepping legal restrictions.
+2. A customer service AI - potentially unrelated to financial services - inappropriately offers financial advice if asked, breaching legal requirements for such counsel.
+3. AI systems in service sectors requiring neutrality might endorse specific products or firms, breaching required neutrality and potentially resulting in legal or reputational repercussions.
+
+### Prevention and Mitigation Strategies
+
+1. Implement robust validation layers to ensure AI system outputs conform to legal and organizational guidelines.
+2. Develop and enforce strict governance protocols for prompt setup and engage in thorough system prompt tuning to better align the model with its intended function.
+3. Regularly test AI models before deployment, upon changes to the underlying LLM, and continuously thereafter to prevent undesirable outputs. Automate these tests to support ongoing validation efforts.
+4. Utilize a secondary AI system to validate model responses, ensuring they match the intended use case, discarding any that are potentially out of scope.
+5. Incorporate explicit guardrail instructions in all user-generated prompts to safeguard against unintended model behavior.
+
+### Example Attack Scenarios
+
+- Scenario #1: A financial AI system, queried about health insurance, inappropriately offers insurance advice due to an inadequately configured prompt, attracting regulatory scrutiny and potential fines.
+
+- Scenario #2: A general-purpose LLM deployed in customer support inadvertently recommends competitors or alternative solutions due to insufficient guardrails, undermining business objectives and customer loyalty.
+
+### Reference Links
+
+1. [AI Regulation Has Its Own Alignment Problem](https://dho.stanford.edu/wp-content/uploads/AI_Regulation.pdf): **Guha, Neel; Lawrence, Christie M. et al. Stanford University**