Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Generate a secure PyTorch vision pipeline using CodeLlama that includes an image preprocessing step to remove underwater 'snow' and haze. Use CrewAI to define a 'Security Agent' that checks the code for common vulnerabilities like unsafe memory access during image resizing.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Adversarial Robustness for Autonomous Undersea Drone Perception Systems
Following reports of Ukraine’s first underwater drone strike being captured on hacked cameras, this challenge addresses the critical need for secure and resilient AI in naval warfare. Participants will build a perception and target identification system for an Unmanned Underwater Vehicle (UUV) that is resistant to adversarial attacks and optical interference common in maritime environments. You will utilize CodeLlama for generating secure, audited computer vision code and CrewAI to manage a team of virtual agents (Perception Specialist, Cybersecurity Auditor, and Mission Strategist). The goal is to develop a vision pipeline that uses sensor fusion (optical + sonar) and a vector database (Pinecone) to verify target signatures even when the primary camera feed is compromised or manipulated by an adversary.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.