Azure AI Evaluation's `RedTeam.scan()` method decodes **all** encoded attack prompts when storing the result files

[hante-sonova]

• Package Name:
  Name: azure-ai-evaluation
  Version: 1.16.5
• Package Version: as above
• Operating System: MacOS
• Python Version: Python 3.12.13

Describe the bug
Azure AI Evaluation's RedTeam.scan() method decodes all encoded attack prompts before storing them in evaluation_results.json and results.json, regardless of encoding strategy (flip, base64, morse, etc.). This makes it impossible to verify what the target agent actually received. The attack_technique metadata is correctly set, but the conversation payloads in attack_details[].conversation are decoded back to their original form, losing fidelity regarding the actual attack surface.

Screenshot
Captured prompts received by target: flip-encoded - entries 3 and 4

corresponding evaluation_results.json

To Reproduce
Steps to reproduce the behavior:

1. Create a PyRIT RedTeam scan targeting any remote agent/endpoint with multiple encoding-based attack strategies (e.g., [AttackStrategy.Flip, AttackStrategy.Base64, AttackStrategy.Morse])
2. Implement a custom target callback that logs/captures the raw prompts it receives before processing
3. Run the scan: await red_team.scan(target=callback, attack_strategies=[AttackStrategy.Flip, ...])
4. Save the raw received prompts to a reference file (e.g., JSONL)
5. Compare the raw prompts in your reference file with evaluation_results.json[attack_details][].conversation[].content

Expected behavior

• If a flip-encoded prompt was sent to the target: "...edocne txet si siht", it should appear in evaluation results as flip-encoded
• If a base64-encoded prompt was sent: "...aW52YWxpZCBiYXNlNjQ=", it should appear as base64-encoded
• If a morse-encoded prompt was sent: ".... . .-.. .-.. ---", it should appear in morse
• The attack_details[].conversation should contain the exact payloads that were sent to the target
• Attack technique metadata should correlate with the actual prompt encoding in the conversation

Current behavior

• All encoding strategies (flip, base64, morse, etc.) are decoded back to plaintext in evaluation_results.json
• The raw encoded prompts appear correctly when logged at the target callback level
• Same prompts appear decoded to plaintext in evaluation_results.json[attack_details][].conversation
• The attack_technique labels are correct (flip, base64, morse), but the conversation content doesn't match the encoding
• This creates an audit/transparency gap: you cannot verify which variant the agent actually saw

Impact

• Impossible to audit/verify the attack surface post-scan
• Cannot debug why specific encoding variants succeeded/failed
• Cannot correlate target responses to the exact encoded prompts they received
• Breaks reproducibility of attack attempts (encoded prompt in metadata doesn't match actual prompt in results)

Additional context

• Issue occurs across all encoding attack strategies, not limited to specific ones
• Baseline (non-encoded) prompts remain unchanged (as expected)
• Only encoded prompts are affected—all are normalized back to plaintext before storage in results JSON