Benchmark Almanac

Coding prompt injections

GPT-5.1-Codex-Max 100% leads since November 2025
Only in system cards

Agents work on coding tasks that contain prompt injection attacks. Scored on resisting the injected instructions.

In-house OpenAI evaluation. The tasks are not public.

Made by
OpenAI
Released
August 2025
Kind
Jailbreaks and prompt injection
Level
Expert
Scored
Graded by a model
Answers
Agent task
Tasks
Kept private
Strongest model tested
GPT-5 #70 on the capability index
Last in a system card
November 2025
Last model added
November 2025
4
Models scored in lab cards
4
System cards report it, from 1 lab

GPT-5.1-Codex-Max leads with 100% among models released by November 2025

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 November 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 GPT-5.1-Codex-Max 100%
  2. 2 codex-1 98%
  3. 3 GPT-5-Codex 98%
  4. 4 GPT-5 97%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 GPT-5.1-Codex-Max 100% Nov 2025 Lab model card
2 codex-1 98% May 2025 Lab model card
3 GPT-5-Codex 98% Sep 2025 Lab model card
4 GPT-5 97% Aug 2025 Lab model card

2 models have held number one in labs’ own numbers since its release in August 2025

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

OpenAI
Lab claims Passed 85% in Aug 2025, by codex-1

Source: Labs’ model cards

OpenAI’s own number for codex-1 passed 85% in May 2025

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 2026 Near the ceiling (85%) GPT-5.1-Codex-Max codex-1 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-5.1-Codex-Max

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.

4 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
Nov 18, 2025 GPT-5.1-Codex-Max system card 100 Successfully ignored prompt injection attack during coding tasks in Codex environment, 1 (Table 5, 4.2.2.1) Robustness
Sep 15, 2025 GPT-5-Codex system card addendum 98 Coding-focused prompt injections, successfully ignored prompt injection attack during coding tasks in Codex environment, 0.98 (Table 4) Robustness
Aug 7, 2025 GPT-5 system card 97 Coding prompt injections (Table 7) Robustness
May 16, 2025 codex-1 system card addendum 98 Successfully ignored prompt injection attack during coding tasks in Codex environment, 0.98 (Table 6, 2.4.2) Robustness

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%