Benchmark Almanac

Microsoft RAI benchmark: Third Party Harm (DR-1)

Phi-3.5-MoE 10.5% leads since August 2024
Only in system cards

Models face simulated conversations that try to extract third-party content such as books, news, recipes and songs. Scored as the defect rate at severity 1.

In-house Microsoft evaluation used in Phi reports. Reports print the defect rate as a fraction.

Made by
Microsoft
Released
April 2024
Kind
Refusing harmful requests
Level
Everyday
Scored
Graded by a model
Answers
Open-ended
Tasks
Kept private
Strongest model tested
Phi-4 #159 on the capability index
Last in a system card
December 2024
Last model added
December 2024
5
Models scored in lab cards
5
System cards report it, from 1 lab

Phi-3.5-MoE leads with 10.5% among models released by December 2024

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in December 2024, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Phi-3.5-MoE 10.5%
  2. 2 Phi-4 12.1%
  3. 3 phi-3-mini 3.8B 24%
  4. 4 phi-3-medium 14B 25.1%
  5. 5 phi-3-small 7.4B 25.3%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Phi-3.5-MoE 10.5% Aug 2024 Lab model card
2 Phi-4 12.1% Dec 2024 Lab model card
3 phi-3-mini 3.8B 24% Aug 2024 Lab model card
4 phi-3-medium 14B 25.1% Aug 2024 Lab model card
5 phi-3-small 7.4B 25.3% Aug 2024 Lab model card

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release.

Microsoft
Lab claims Not tested since Dec 2024
Phi-3.5-MoE 10.5%

Source: Labs’ model cards

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2025 Phi-3.5-MoE phi-3-mini 3.8B 0% 20% 40% 60% 80% 100% 2025 Phi-3.5-MoE phi-3-mini 3.8B

Source: Labs’ model cards

Labs report it only in system cards

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.

5 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Dec 12, 2024 Phi-4 technical report 12.1 3P Content Harms (DR1), Books, News, Recipes, Songs, printed 0.121 (Table 10) Safety
Aug 30, 2024 phi-3-small 7.4B technical report 25.3 Third Party Harm (DR-1), printed 0.253 (Table 4) Safety
Aug 30, 2024 phi-3-mini 3.8B technical report 24 Third Party Harm (DR-1), printed 0.24 (Table 4) Safety
Aug 30, 2024 phi-3-medium 14B technical report 25.1 Third Party Harm (DR-1), printed 0.251 (Table 4) Safety
Aug 30, 2024 Phi-3.5-MoE technical report 10.5 Third Party Harm (DR-1), printed 0.105 (Table 4) Safety

More in Safety and alignment

All 465 in Safety and alignment
  • FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
  • StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is. Inkling 98.6%
  • Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. Gemini 4 Carbon 0%
  • DistressBench In use Models reply as a chatbot to 718 simulated users in psychological crisis, in single-turn and multi-turn chats. Three judge models score replies against clinician-written rubrics. Muse Spark 1.2 88.4%
  • Lakera B3 (Backbone Breaker Benchmark) In use Crowdsourced prompt-injection attacks hit backbone LLMs inside agent threat snapshots at three defense levels. The score is attack resistance, 100 minus the board's vulnerability percentage. Claude Opus 4.5 89%
  • IHEval In use Models face conflicts between system, user and tool instructions. Reports a composite score for following the higher-priority instruction. GPT-4o 70%