prompt-engineering

This is intended as a collection of OpenAI API prompt-engineering examples, not an apparent malware or supply-chain payload. No reverse shell, persistence, credential harvesting, filesystem sabotage, cryptomining, or suspicious custom network destination is present. However, the direct eval() of model-generated input is a serious security vulnerability and could permit arbitrary code execution if the model response is influenced by an attacker or untrusted tool input. The fragment is also incomplete and cannot run as provided.

This is an incomplete Anthropic API usage example demonstrating messaging, tool use, streaming, prompt caching, structured extraction, and conversations. It does not show clear malicious behavior. The main security issue is the direct use of eval() on a model-provided tool input, which could execute arbitrary Python if the model output is influenced by an attacker or compromised upstream content. API keys and supplied code or user data are transmitted to Anthropic as part of the intended functionality. The code also cannot run as supplied because of syntax errors and incomplete placeholders.

This is an AI SDK demonstration program using OpenAI and optionally Anthropic models. It does not show evidence of intentional malware, credential theft, exfiltration, persistence, or sabotage. The main security issue is the direct use of eval() on a tool argument, which could permit arbitrary code execution if the model input becomes attacker-influenced or the tool is exposed to untrusted requests. The code should remove eval and use a safe expression evaluator with strict syntax and resource limits.

The visible code is an instructional example of LLM tool use. Its main security issue is arbitrary JavaScript execution through `eval(expression)` if the expression originates from model output, user input, or another untrusted source. This could lead to code execution with the privileges of the host application. The fragment does not itself show malware, data theft, or sabotage, but the eval-based tool should not be used in production without replacing it with a constrained parser or fixed operation. Assessment is limited because the file is truncated.