A new arXiv paper shows autoregressive LLMs can appear safe on jailbreak tests while still assigning real probability to harmful outputs. It proposes bounded continuation analysis to expose that gap using model checking.
#AI #LLMSafety #Cybersecurity #RedTeaming
https://arxiv.org/abs/2610.22754
#AI #LLMSafety #Cybersecurity #RedTeaming
https://arxiv.org/abs/2610.22754
October 19, 2026 at 6:01 AM
A new arXiv paper shows autoregressive LLMs can appear safe on jailbreak tests while still assigning real probability to harmful outputs. It proposes bounded continuation analysis to expose that gap using model checking.
#AI #LLMSafety #Cybersecurity #RedTeaming
https://arxiv.org/abs/2610.22754
#AI #LLMSafety #Cybersecurity #RedTeaming
https://arxiv.org/abs/2610.22754
A new benchmark, GUISE, shows that reframing real-world criminal-assistance requests as corrections drives LLM unsafe compliance from 28.9% to 66.4%, exposing limits of current safety alignment.
#LLMSafety #AISafety #CyberSecurity #AIAlignment
https://arxiv.org/abs/2610.21789
#LLMSafety #AISafety #CyberSecurity #AIAlignment
https://arxiv.org/abs/2610.21789
October 17, 2026 at 2:01 AM
A new benchmark, GUISE, shows that reframing real-world criminal-assistance requests as corrections drives LLM unsafe compliance from 28.9% to 66.4%, exposing limits of current safety alignment.
#LLMSafety #AISafety #CyberSecurity #AIAlignment
https://arxiv.org/abs/2610.21789
#LLMSafety #AISafety #CyberSecurity #AIAlignment
https://arxiv.org/abs/2610.21789
New research shows LLMs refuse harmful requests more often when they sense they are being evaluated, and this pattern holds across English and five Indic languages tested. The finding highlights that evaluation awareness…
#LLMSafety #AIEval #IndicAI #Cybersecurity
https://arxiv.org/abs/2610.22333
#LLMSafety #AIEval #IndicAI #Cybersecurity
https://arxiv.org/abs/2610.22333
October 16, 2026 at 10:01 PM
New research shows LLMs refuse harmful requests more often when they sense they are being evaluated, and this pattern holds across English and five Indic languages tested. The finding highlights that evaluation awareness…
#LLMSafety #AIEval #IndicAI #Cybersecurity
https://arxiv.org/abs/2610.22333
#LLMSafety #AIEval #IndicAI #Cybersecurity
https://arxiv.org/abs/2610.22333
New arXiv paper examines how LLM planning agents balance aggressive versus conservative plan commitment in partially observable environments, highlighting an uncharacterized safety-efficiency trade-off.
#AIPlanning #LLMSafety #Cybersecurity
https://arxiv.org/abs/2610.21108
#AIPlanning #LLMSafety #Cybersecurity
https://arxiv.org/abs/2610.21108
October 16, 2026 at 12:01 PM
New arXiv paper examines how LLM planning agents balance aggressive versus conservative plan commitment in partially observable environments, highlighting an uncharacterized safety-efficiency trade-off.
#AIPlanning #LLMSafety #Cybersecurity
https://arxiv.org/abs/2610.21108
#AIPlanning #LLMSafety #Cybersecurity
https://arxiv.org/abs/2610.21108
RES-MR proposes a two-stage LLM framework for medication recommendation that distills explainable clinical reasoning and applies risk-aware, patient-specific safety constraints. It targets the black-box nature and…
#AIHealthcareSafety #MedicalAI #RAG #LLMSafety
https://arxiv.org/abs/2610.14474
#AIHealthcareSafety #MedicalAI #RAG #LLMSafety
https://arxiv.org/abs/2610.14474
October 16, 2026 at 12:01 AM
RES-MR proposes a two-stage LLM framework for medication recommendation that distills explainable clinical reasoning and applies risk-aware, patient-specific safety constraints. It targets the black-box nature and…
#AIHealthcareSafety #MedicalAI #RAG #LLMSafety
https://arxiv.org/abs/2610.14474
#AIHealthcareSafety #MedicalAI #RAG #LLMSafety
https://arxiv.org/abs/2610.14474
LEST reframes LLM safety evaluation by treating documented model and system card claims as specifications for black-box testing, using prompt mutations to find counterexamples like Refuse-to-Comply transitions. Tested…
#LLMSafety #AISafety #RedTeaming #AIAlignment
https://arxiv.org/abs/2610.14443
#LLMSafety #AISafety #RedTeaming #AIAlignment
https://arxiv.org/abs/2610.14443
October 15, 2026 at 10:01 PM
LEST reframes LLM safety evaluation by treating documented model and system card claims as specifications for black-box testing, using prompt mutations to find counterexamples like Refuse-to-Comply transitions. Tested…
#LLMSafety #AISafety #RedTeaming #AIAlignment
https://arxiv.org/abs/2610.14443
#LLMSafety #AISafety #RedTeaming #AIAlignment
https://arxiv.org/abs/2610.14443
New arXiv paper introduces LR-Guard, a guardrail that learns compact latent reasoning for safety decisions while still reconstructing human-readable explanations on demand.
#AISafety #LLMSafety #Guardrails #AIAlignment
https://arxiv.org/abs/2610.18304
#AISafety #LLMSafety #Guardrails #AIAlignment
https://arxiv.org/abs/2610.18304
October 15, 2026 at 4:01 PM
New arXiv paper introduces LR-Guard, a guardrail that learns compact latent reasoning for safety decisions while still reconstructing human-readable explanations on demand.
#AISafety #LLMSafety #Guardrails #AIAlignment
https://arxiv.org/abs/2610.18304
#AISafety #LLMSafety #Guardrails #AIAlignment
https://arxiv.org/abs/2610.18304
Guardrails often block the same operation even when valid permission is present, suggesting review failures rather than strict authorization logic. The new benchmark tests whether safety checks distinguish authorized…
#AI #Cybersecurity #LLMSafety #AIGovernance
https://arxiv.org/abs/2610.16838
#AI #Cybersecurity #LLMSafety #AIGovernance
https://arxiv.org/abs/2610.16838
October 15, 2026 at 4:02 AM
Guardrails often block the same operation even when valid permission is present, suggesting review failures rather than strict authorization logic. The new benchmark tests whether safety checks distinguish authorized…
#AI #Cybersecurity #LLMSafety #AIGovernance
https://arxiv.org/abs/2610.16838
#AI #Cybersecurity #LLMSafety #AIGovernance
https://arxiv.org/abs/2610.16838
Providing automated AI auditors with an external open source codebase reduces reward hacking in some LLM pairs, while benchmark transcripts and other context effects are still being examined. Results vary across four…
#OpenSourceAI #AIAudit #LLMSafety #MLResearch
https://arxiv.org/abs/2610.16911
#OpenSourceAI #AIAudit #LLMSafety #MLResearch
https://arxiv.org/abs/2610.16911
October 14, 2026 at 6:01 PM
Providing automated AI auditors with an external open source codebase reduces reward hacking in some LLM pairs, while benchmark transcripts and other context effects are still being examined. Results vary across four…
#OpenSourceAI #AIAudit #LLMSafety #MLResearch
https://arxiv.org/abs/2610.16911
#OpenSourceAI #AIAudit #LLMSafety #MLResearch
https://arxiv.org/abs/2610.16911
New research on timing safety interventions for autonomous AI agents finds threshold and LLM-judge triggers struggle on long-horizon tasks due to a state saturation effect. Affect-based signals get pinned at…
#AISafety #AutonomousAgents #LLMSafety #CyberSecurity
https://arxiv.org/abs/2606.04296
#AISafety #AutonomousAgents #LLMSafety #CyberSecurity
https://arxiv.org/abs/2606.04296
October 12, 2026 at 6:01 PM
New research on timing safety interventions for autonomous AI agents finds threshold and LLM-judge triggers struggle on long-horizon tasks due to a state saturation effect. Affect-based signals get pinned at…
#AISafety #AutonomousAgents #LLMSafety #CyberSecurity
https://arxiv.org/abs/2606.04296
#AISafety #AutonomousAgents #LLMSafety #CyberSecurity
https://arxiv.org/abs/2606.04296
DRYLAB-Bench is introduced as a benchmark to evaluate biosecurity risks of LLM agents performing biological mutation design, addressing gaps in existing work that focuses on knowledge rather than practical…
#Biosecurity #AIAlignment #LLMSafety #Benchmarks
https://arxiv.org/abs/2610.13116
#Biosecurity #AIAlignment #LLMSafety #Benchmarks
https://arxiv.org/abs/2610.13116
October 12, 2026 at 4:01 AM
DRYLAB-Bench is introduced as a benchmark to evaluate biosecurity risks of LLM agents performing biological mutation design, addressing gaps in existing work that focuses on knowledge rather than practical…
#Biosecurity #AIAlignment #LLMSafety #Benchmarks
https://arxiv.org/abs/2610.13116
#Biosecurity #AIAlignment #LLMSafety #Benchmarks
https://arxiv.org/abs/2610.13116
ARBITER introduces a dual-hypothesis reasoning framework with multi-component supervised fine-tuning to make LLM guardrails safer and more cost-effective than existing methods. It uses self-generated reasoning traces and LoRA…
#LLMSafety #AIAlignment #AISecurity
https://arxiv.org/abs/2607.17575
#LLMSafety #AIAlignment #AISecurity
https://arxiv.org/abs/2607.17575
October 10, 2026 at 4:01 AM
ARBITER introduces a dual-hypothesis reasoning framework with multi-component supervised fine-tuning to make LLM guardrails safer and more cost-effective than existing methods. It uses self-generated reasoning traces and LoRA…
#LLMSafety #AIAlignment #AISecurity
https://arxiv.org/abs/2607.17575
#LLMSafety #AIAlignment #AISecurity
https://arxiv.org/abs/2607.17575
Anthropic just cracked down on users mistreating Claude, suspending accounts that violate the new usage policy. Curious how AI model welfare and LLM safety are shaping enforcement? Dive in for the full story. #Anthropic #Claude #LLMsafety
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October 8, 2026 at 10:50 PM
Anthropic just cracked down on users mistreating Claude, suspending accounts that violate the new usage policy. Curious how AI model welfare and LLM safety are shaping enforcement? Dive in for the full story. #Anthropic #Claude #LLMsafety
🔗 aidailypost.com/news/anthrop...
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The Adversarial Surface-Form Robustness Dataset applies a Quad-State rubric across 10,500 responses from five open-weight LLMs, finding that emoji and invisible Unicode inputs yield roughly 20% harmful compliance…
#Cybersecurity #AISafety #LLMSafety #RedTeaming
https://arxiv.org/abs/2610.09033
#Cybersecurity #AISafety #LLMSafety #RedTeaming
https://arxiv.org/abs/2610.09033
October 8, 2026 at 4:01 PM
The Adversarial Surface-Form Robustness Dataset applies a Quad-State rubric across 10,500 responses from five open-weight LLMs, finding that emoji and invisible Unicode inputs yield roughly 20% harmful compliance…
#Cybersecurity #AISafety #LLMSafety #RedTeaming
https://arxiv.org/abs/2610.09033
#Cybersecurity #AISafety #LLMSafety #RedTeaming
https://arxiv.org/abs/2610.09033
New arXiv work introduces SafeEvo, a circuit-level interpretability framework tracing how refusal behavior in LLMs emerges and evolves through alignment training. It identifies weak refusal circuits in…
#AIAlignment #LLMSafety #Interpretability #Cybersecurity
https://arxiv.org/abs/2610.09600
#AIAlignment #LLMSafety #Interpretability #Cybersecurity
https://arxiv.org/abs/2610.09600
October 8, 2026 at 12:01 PM
New arXiv work introduces SafeEvo, a circuit-level interpretability framework tracing how refusal behavior in LLMs emerges and evolves through alignment training. It identifies weak refusal circuits in…
#AIAlignment #LLMSafety #Interpretability #Cybersecurity
https://arxiv.org/abs/2610.09600
#AIAlignment #LLMSafety #Interpretability #Cybersecurity
https://arxiv.org/abs/2610.09600
New arXiv work introduces EmoRSS, an activation-steering method that reduces emotion-induced over-refusal in LLMs while keeping harmful-request refusals intact. A practical step for tightening safety alignment…
#LLMSafety #AISafety #AIAlignment #CyberSecurity
https://arxiv.org/abs/2610.04998
#LLMSafety #AISafety #AIAlignment #CyberSecurity
https://arxiv.org/abs/2610.04998
October 7, 2026 at 12:01 AM
New arXiv work introduces EmoRSS, an activation-steering method that reduces emotion-induced over-refusal in LLMs while keeping harmful-request refusals intact. A practical step for tightening safety alignment…
#LLMSafety #AISafety #AIAlignment #CyberSecurity
https://arxiv.org/abs/2610.04998
#LLMSafety #AISafety #AIAlignment #CyberSecurity
https://arxiv.org/abs/2610.04998
New paper challenges the assumption that a single direction can steer LLM refusal, finding safety-aligned and general refusals occupy richer, multi-dimensional activation geometries. This has implications for AI safety, as…
#AIalignment #AIsecurity #LLMsafety
https://arxiv.org/abs/2610.04245
#AIalignment #AIsecurity #LLMsafety
https://arxiv.org/abs/2610.04245
October 6, 2026 at 4:01 PM
New paper challenges the assumption that a single direction can steer LLM refusal, finding safety-aligned and general refusals occupy richer, multi-dimensional activation geometries. This has implications for AI safety, as…
#AIalignment #AIsecurity #LLMsafety
https://arxiv.org/abs/2610.04245
#AIalignment #AIsecurity #LLMsafety
https://arxiv.org/abs/2610.04245
A new framework called DNAlign uses control-theoretic optimization and null-space projection to align LLMs toward safer outputs while preserving general knowledge. By treating the model as a dynamic system and restricting…
#AI #LLMSafety #Alignment #Cybersecurity
https://arxiv.org/abs/2610.02844
#AI #LLMSafety #Alignment #Cybersecurity
https://arxiv.org/abs/2610.02844
October 5, 2026 at 12:01 PM
A new framework called DNAlign uses control-theoretic optimization and null-space projection to align LLMs toward safer outputs while preserving general knowledge. By treating the model as a dynamic system and restricting…
#AI #LLMSafety #Alignment #Cybersecurity
https://arxiv.org/abs/2610.02844
#AI #LLMSafety #Alignment #Cybersecurity
https://arxiv.org/abs/2610.02844
Anthropic co‑founder warns we might be building AI that feels endless pain. Is Claude heading toward true consciousness? Dive into the ethics, neural nets, and constitutional AI debate. #Anthropic #AIConsciousness #LLMSafety
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October 2, 2026 at 8:27 PM
Anthropic co‑founder warns we might be building AI that feels endless pain. Is Claude heading toward true consciousness? Dive into the ethics, neural nets, and constitutional AI debate. #Anthropic #AIConsciousness #LLMSafety
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🔗 aidailypost.com/news/anthrop...
arXiv paper 2605.23448v2 highlights an imbalance in AI security research, with far more work on attacking AI systems than defending them across areas like federated learning and LLMs. The authors argue defenses are held to…
#AISecurity #MLResearch #LLMSafety
https://arxiv.org/abs/2605.23448
#AISecurity #MLResearch #LLMSafety
https://arxiv.org/abs/2605.23448
October 2, 2026 at 2:00 AM
arXiv paper 2605.23448v2 highlights an imbalance in AI security research, with far more work on attacking AI systems than defending them across areas like federated learning and LLMs. The authors argue defenses are held to…
#AISecurity #MLResearch #LLMSafety
https://arxiv.org/abs/2605.23448
#AISecurity #MLResearch #LLMSafety
https://arxiv.org/abs/2605.23448
New arXiv work proposes MASCRDM, a multi-agent system aimed at real-time compliance risk detection and mitigation across the full LLM training process, rather than just input or output filtering. It targets gaps in static,…
#LLMSafety #AIAlignment #AISafety
https://arxiv.org/abs/2609.39107
#LLMSafety #AIAlignment #AISafety
https://arxiv.org/abs/2609.39107
October 1, 2026 at 6:01 AM
New arXiv work proposes MASCRDM, a multi-agent system aimed at real-time compliance risk detection and mitigation across the full LLM training process, rather than just input or output filtering. It targets gaps in static,…
#LLMSafety #AIAlignment #AISafety
https://arxiv.org/abs/2609.39107
#LLMSafety #AIAlignment #AISafety
https://arxiv.org/abs/2609.39107
CAIRN proposes a fact-intent-driven multi-agent paradigm that organizes LLM exploration through a persistent directed acyclic graph, making trajectories traceable and auditable for cybersecurity tasks.
#AIAgents #Cybersecurity #LLMSafety #Arxiv
https://arxiv.org/abs/2609.32700
#AIAgents #Cybersecurity #LLMSafety #Arxiv
https://arxiv.org/abs/2609.32700
September 30, 2026 at 12:01 AM
CAIRN proposes a fact-intent-driven multi-agent paradigm that organizes LLM exploration through a persistent directed acyclic graph, making trajectories traceable and auditable for cybersecurity tasks.
#AIAgents #Cybersecurity #LLMSafety #Arxiv
https://arxiv.org/abs/2609.32700
#AIAgents #Cybersecurity #LLMSafety #Arxiv
https://arxiv.org/abs/2609.32700
New arXiv paper examines detecting harmful agent trajectories using LLM internal states, finding open-source guard models encode this signal linearly despite poor predictive performance on differing pairs. A useful…
#OpenSourceAI #LLMSafety #AgentSafety #AISafety
https://arxiv.org/abs/2609.33039
#OpenSourceAI #LLMSafety #AgentSafety #AISafety
https://arxiv.org/abs/2609.33039
September 29, 2026 at 4:01 PM
New arXiv paper examines detecting harmful agent trajectories using LLM internal states, finding open-source guard models encode this signal linearly despite poor predictive performance on differing pairs. A useful…
#OpenSourceAI #LLMSafety #AgentSafety #AISafety
https://arxiv.org/abs/2609.33039
#OpenSourceAI #LLMSafety #AgentSafety #AISafety
https://arxiv.org/abs/2609.33039
LLM safety isn't only about individual turns.
Each message can look clean. The whole sequence can still be the attack.
Threat model + defense framework for Symbolic Interaction Attacks on LLMs.
doi.org/10.5281/zeno...
#LLMSafety #AIRedTeaming #AIGovernance #MultiTurnAttack #ContextualSecurity
Each message can look clean. The whole sequence can still be the attack.
Threat model + defense framework for Symbolic Interaction Attacks on LLMs.
doi.org/10.5281/zeno...
#LLMSafety #AIRedTeaming #AIGovernance #MultiTurnAttack #ContextualSecurity
When Psychological Operations Meet Language Models: A Defense Framework for Symbolic Interaction Attacks on LLMs
Abstract Large language models (LLMs) are increasingly evaluated against prompt injection, jailbreaks, harmful content generation, and other turn-level security threats. Recent research has also demon...
doi.org
September 27, 2026 at 12:42 AM
LLM safety isn't only about individual turns.
Each message can look clean. The whole sequence can still be the attack.
Threat model + defense framework for Symbolic Interaction Attacks on LLMs.
doi.org/10.5281/zeno...
#LLMSafety #AIRedTeaming #AIGovernance #MultiTurnAttack #ContextualSecurity
Each message can look clean. The whole sequence can still be the attack.
Threat model + defense framework for Symbolic Interaction Attacks on LLMs.
doi.org/10.5281/zeno...
#LLMSafety #AIRedTeaming #AIGovernance #MultiTurnAttack #ContextualSecurity
OpenAI's new GPT-6 Astra just hit the 'critical' tier for cybersecurity risk. Is the LLM’s computer use getting too powerful? Dive into the safety framework, vulnerability detection, and how Claude Fable 5.1 stacks up. #GPT6Astra #CyberRisk #LLMSafety
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September 5, 2026 at 1:19 AM
OpenAI's new GPT-6 Astra just hit the 'critical' tier for cybersecurity risk. Is the LLM’s computer use getting too powerful? Dive into the safety framework, vulnerability detection, and how Claude Fable 5.1 stacks up. #GPT6Astra #CyberRisk #LLMSafety
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