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Named entitynamed-entityNewly observed

RL-ATTACK

arXiv:2611.08711v1 Announce Type: new Abstract: Large language models (LLMs) exhibit vulnerabilities to adversarial perturbations that can induce unintended responses. These vulnerabilities can be identified via intentional attacks and subsequently used to improve model safety. In black-box settings where gradients are inaccessible, existing methods tend to

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First observed in our collection
Nov 9, 2026, 6:00 AM UTC
Earliest cited publication
Nov 9, 2026, 5:00 AM UTC
Last observed
Nov 9, 2026, 6:00 AM UTC

Evidence in the last complete seven days

Source mentions
1
Previous seven days 0
Distinct sources
1
Previous 0
Comparable growth
Not comparable
Ranking appearances
0

Collection observations and undated records are separate from dated publication counts. Observed in 7d:1 · Undated records:0

Collection coverage · Comparable windowsCurrent / previous observed days 7 / 7

Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.

Current window:2026-11-03 → 2026-11-10 (end excluded) · History 22 days

Observed source coverage by seven-day window
SourceCurrent daysPrevious daysLast success
arXiv76Nov 9, 2026, 1:15 AM UTC
arXiv77Nov 9, 2026, 6:00 AM UTC
Ask HN77Nov 9, 2026, 7:00 PM UTC
Ask HN77Nov 10, 2026, 12:45 AM UTC
Ask HN77Nov 10, 2026, 12:45 AM UTC
Ask HN77Nov 10, 2026, 12:45 AM UTC
GitHub77Nov 9, 2026, 2:15 PM UTC
GitHub Issues77Nov 9, 2026, 4:45 PM UTC
GitHub Releases76Nov 9, 2026, 1:30 PM UTC
GitHub77Nov 9, 2026, 3:30 PM UTC
GitHub Issues77Nov 9, 2026, 6:15 PM UTC
GitHub Releases77Nov 9, 2026, 6:15 PM UTC
GitHub77Nov 9, 2026, 3:30 PM UTC
GitHub Issues77Nov 9, 2026, 6:15 PM UTC
GitHub Releases77Nov 9, 2026, 6:15 PM UTC
GitHub Discovery50Nov 10, 2026, 12:45 AM UTC
GitHub Trending77Nov 9, 2026, 8:45 PM UTC
Google77Nov 9, 2026, 9:00 AM UTC
Hugging Face77Nov 9, 2026, 9:00 AM UTC
Hugging Face Spaces50Nov 10, 2026, 12:00 AM UTC
Hacker News77Nov 9, 2026, 8:45 PM UTC
Hacker News77Nov 10, 2026, 12:45 AM UTC
Hacker News77Nov 9, 2026, 7:00 PM UTC
Hacker News50Nov 10, 2026, 12:45 AM UTC
Show HN50Nov 10, 2026, 12:45 AM UTC
MIT News67Nov 10, 2026, 12:45 AM UTC
OpenAI77Nov 9, 2026, 12:00 PM UTC
Product Hunt77Nov 9, 2026, 8:45 PM UTC
量子位57Nov 9, 2026, 11:45 AM UTC
OpenRouter50Nov 10, 2026, 12:16 AM UTC
GitHub Trending47Nov 6, 2026, 1:15 PM UTC
Hugging Face Models50Nov 10, 2026, 12:42 AM UTC
HF Daily Papers50Nov 10, 2026, 12:42 AM UTC
OpenRouter50Nov 10, 2026, 12:16 AM UTC
Product Hunt47Nov 6, 2026, 1:15 PM UTC
SEC EDGAR77Nov 9, 2026, 11:30 PM UTC
Sequoia Capital77Nov 9, 2026, 7:15 AM UTC
Simon Willison50Nov 9, 2026, 4:00 PM UTC
TechCrunch77Nov 9, 2026, 11:00 PM UTC
Contracts Finder77Nov 9, 2026, 9:00 PM UTC
V2EX77Nov 9, 2026, 11:00 PM UTC
  • Counts describe monitored public sources, not the whole web or search demand.

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

2026-10-272026-11-10
View accessible daily data
Daily source mentions and collection observations
UTC dateMentionsSourcesObservationsRanking appearances
2026-10-270000
2026-10-280000
2026-10-290000
2026-10-300000
2026-10-310000
2026-11-010000
2026-11-020000
2026-11-030000
2026-11-040000
2026-11-050000
2026-11-060000
2026-11-070000
2026-11-080000
2026-11-091110
2026-11-100000
Source page screenshot: RL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement Learning
Source page screenshot · arxiv.org

Source evidence

Sources in the evidence shown:arXiv 1

  1. arXiv · Published Nov 9, 2026

    RL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement Learning

    arXiv:2611.08711v1 Announce Type: new Abstract: Large language models (LLMs) exhibit vulnerabilities to adversarial perturbations that can induce unintended responses. These vulnerabilities can be identified via intentional attacks and subsequently used to improve model safety. In black-box settings where gradients are inaccessible, existing methods tend to

    View source record →

Review priority 39 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

  • Evidence

    8 / 30

    Distinct dated articles or first observations; repeat crawling adds nothing.

  • Publisher diversity

    8 / 25

    Conservative owner grouping; syndicated copies count once.

  • Comparable change

    0 / 20

    Comparable complete weeks only; no percentage from a zero baseline.

  • Recent evidence

    14.2 / 15

    Latest publication or first observation, not repeated fetch time.

  • Name specificity

    8.5 / 10

    Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

  • First observation is a collection milestone, not proof that a word or product is new to the world.
  • Counts describe monitored public sources, not the whole web or search demand.
  • First observed here is not the invention date of a name or idea.
  • Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
  • Owner grouping is conservative; undisclosed syndication may remain.
  • The previous week is too small for a reliable percentage change.
  • Current evidence comes from one publisher or owner.

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