mlsanityData to 14 Oct 2026

ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation

arXiv 2609.13737 ↗First posted 12 Sep 2026cs.CLcited by 0

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

As large language models (LLMs) are increasingly deployed, the generation of harmful content has become a critical safety concern. Existing safeguards operate at the input, output, or streaming-generation stages, while early-risk methods that rely on surface tokens or output logits may suffer from weak initial signals, and internals-based detectors using dense representations may retain highly entangled and redundant safety-irrelevant information. It therefore remains unclear whether the earliest post-generation hidden states already contain reliable signals about final-response harmfulness. To address this gap, we propose ForeSight, a first-token output-risk forecasting framework that distills weak and redundant early safety signals into compact, layer-aware risk representations. Experiments on five safety benchmarks and two target models demonstrate that ForeSight achieves superior and efficient early-risk forecasting while relying solely on first-token hidden states. The code is available at: https://github.com/Scabbards1500/Foresight

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 28 of its 39 references that are in the map. First posted on arXiv 12 Sep 2026.

Builds on

28 of 39 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025Beyond Linear Probes: Dynamic Safety Monitoring for Language Models30 Sep
  2. 2025LLMs Encode Harmfulness and Refusal Separately16 Jul
  3. 2025From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring11 Jun
  4. 2025Advancing LLM Safe Alignment with Safety Representation Ranking21 May
  5. 2025Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails15 Jan
  6. 2024Legilimens: Practical and Unified Content Moderation for Large Language Model Services28 Aug
  7. 2024WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs26 Jun
  8. 2024SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models18 Jun
  9. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deep10 Jun
  10. 2024Improving Alignment and Robustness with Circuit Breakers6 Jun

Other references · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  4. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  5. 2016Understanding intermediate layers using linear classifier probescited by 1,353
  6. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
  7. 2023Finding Neurons in a Haystack: Case Studies with Sparse Probingcited by 334
  8. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 243
  9. 2023ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversationcited by 231
  10. 2020HateBERT: Retraining BERT for Abusive Language Detection in Englishcited by 207
  11. 2023Linearity of Relation Decoding in Transformer Language Modelscited by 200
  12. 2024Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmeticcited by 112
6 more
  1. 2024How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden Statescited by 98
  2. 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
  3. 2025HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden Statescited by 33
  4. 2024HSF: Defending against Jailbreak Attacks with Hidden State Filteringcited by 23
  5. 2025Beyond the Surface: Enhancing LLM-as-a-Judge Alignment with Human via Internal Representationscited by 15
  6. 2025LLM Jailbreak Detection for (Almost) Free!cited by 13

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Close in wording, no citation either way

  1. 2026Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack
  2. 2026DANGERLENS: Probing Dangerous Representations Hidden by Safety Alignment
  3. 2026PreAct-Bench: Benchmarking Predictive Monitoring in LLMs
  4. 2025Read the Scene, Not the Script: Outcome-Aware Safety for LLMs
  5. 2026DeepSight: An All-in-One LM Safety Toolkit
  6. 2026From Latent Space to Jacobian Space: Measuring, Evading, and Training Against Safety-Content Accessibility
  7. 2026Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming
  8. 2025Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation