Search: “adaptive-shield”

ShieldCrash PoC: Microsoft Defender Fix Bypass

A ShieldCrash proof-of-concept bypasses Microsoft's patch for CVE-2026-69414, a high-severity elevation-of-privilege flaw in the Microsoft Malware Protection Engine.

Microsoft previously fixed CVE-2026-69414 (ShieldBreak), a high-severity elevation-of-privilege vulnerability in the Microsoft Malware Protection Engine used by Microsoft Defender. SOCRadar now reports a new proof-of-concept dubbed ShieldCrash demonstrating that the published fix can be bypassed. The available source text does not detail affected versions or whether the bypass has been used in real attacks.

SOCRadar·Sep 10, 12:18 UTC · 6d agoExploit / PoC 3 sourcesCVE-2026-69414

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR·Sep 3, 22:44 UTC · 12d agoAI safety & security

EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?

Researchers introduce EVOHARNESSBENCH, a benchmark showing that evolving agent harnesses (tools, skills, agents) cause forgetting and inconsistent adaptation across 802 tasks.

The paper introduces EVOHARNESSBENCH, a benchmark that places non-stationarity in the externally supplied agent harness rather than in the task stream, evaluating agents across tools, skills, and specialist agents. It comprises 17 multi-stage harness streams built deterministically from verifier-based benchmarks, totaling 802 tasks, 520 tools, 42 skills, and 62 agents. Evaluation covers deployment (retention of previously accessible competence) and self-evolving adaptation settings. Results show harness expansion alone degrades previously solved tasks (harness-induced forgetting), adaptation gains are inconsistent, and retention and adaptation can pull in opposite directions.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

New Microsoft Defender 'ShieldCrash' zero-day grants SYSTEM access

Researcher Nightmare Eclipse released 'ShieldCrash', a zero-day exploit for Microsoft Defender that grants attackers SYSTEM-level access.

An anonymous researcher known as Nightmare Eclipse published a zero-day exploit for Microsoft Defender, dubbed 'ShieldCrash', that yields SYSTEM-level access. The release came immediately after Microsoft rolled out its September 2026 Patch Tuesday security updates. No CVE identifier has been assigned publicly and no in-the-wild exploitation has been reported yet. Microsoft Defender ships by default on Windows, so potential exposure is broad until Microsoft patches the flaw.

BleepingComputer·Sep 9, 07:30 UTC · 7d agoExploit / PoC

Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

Researchers introduce Decoy Direction Optimization, a cheap weight-editing defense that blinds refusal-direction ablation attacks against open-weight LLM safety guardrails.

Refusal Feature Ablation bypasses safety guardrails in open-weight LLMs by projecting out a linear refusal direction, often with high attack success rates. Decoy Direction Optimization injects a high-magnitude nonlinear decoy into MLP neurons so attackers' contrastive estimators ablate a harmless orthogonal feature instead. Evaluated across six model families, DDO keeps ASR below 10% under standard RFA and on Llama-3-8B-Instruct reduces Heretic weight-level attack ASR from 88.7% to 18%. It costs 30 to 450 times less per configuration than trained defense baselines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI safety & security

Researcher Drops New Microsoft Defender PoC Showing ShieldBreak Patch Can Be Bypassed

Researcher Chaotic Eclipse released a PoC showing CVE-2026-69414's patch is bypassable, allowing arbitrary file reads as SYSTEM on current Windows.

The researcher known as Chaotic Eclipse published a proof-of-concept for a zero-day in Microsoft Defender, dubbed ShieldCrash, assessed as a patch bypass for ShieldBreak (CVE-2026-69414, CVSS 7.8). The PoC demonstrates an arbitrary file read as SYSTEM with the latest Windows installed, and all supported desktop versions are said to be impacted. Microsoft patched the original issue in Microsoft Malware Protection Engine 1.1.26080.3, which updates automatically. The same researcher recently released PoCs for flaws in CrowdStrike Falcon Sensor, Kaspersky, Avast Antivirus and NVIDIA.

The Hacker News·Sep 9, 06:47 UTC · 7d agoExploit / PoCCVE-2026-694141

Learning to Solve Hard Problems in RL for LLMs by Never Giving Up

Paper introduces Never Give Up adaptive sampling, fixing RL's 'Matthew Effect' where compute is wasted on easy problems and hard problems see little improvement.

Researchers identify a 'Matthew Effect' in reinforcement learning for LLMs, where RL yields large gains on easy problems but minimal improvement on hard ones because compute is misallocated. They propose Never Give Up (NGU), an adaptive sampling method that keeps generating samples for a problem until one is correct, using asynchronous RL to filter easy problems cheaply and concentrate compute on hard ones. NGU improves performance per compute on the Deepscaler math benchmark and iteratively solves the Manufactoria coding task where standard GRPO with per-test reward fails.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents

EvoSafeHarness auto-synthesizes per-model, per-domain safety harnesses, cutting prompt-injection attack success on AgentDojo to 0.0% at 82.8% utility.

EvoSafeHarness is an optimization framework that synthesizes deployable safety harnesses for frozen LLM agents in a target domain, jointly searching natural-language policies and executable code logic guided by model behavior, domain specifications, and adversarial review. On DecodingTrust-Agent it reduces average attack success rate from 45.6% to 10.0% at a 3.3-point utility cost, and on AgentDojo reaches 82.8% utility at 0.0% ASR, twice CaMeL's utility at that operating point. It keeps mean ASR below 20% under adaptive PAIR attacks and transfers unchanged to unseen AgentDyn suites. The analysis finds domain semantics determine required safety relations while model and runtime behavior determine enforcement points.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI safety & security

Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning

Researchers analyze why Preventative Steering protects LLMs against malicious fine-tuning, finding active adaptation drives protection, and propose Progressive Intensity Scheduling.

The paper studies Preventative Steering, a training-time defense that injects undesirable-trait persona vectors during adversarial fine-tuning and removes them at evaluation time. Temporal analysis shows protection emerges from an early compensatory adaptation phase followed by a steady-state phase, with attention output projections acting as the dominant residual-write route for defensive updates. Intervention Delta Preservation experiments show that preserving or reinjecting weight offsets fails to maintain protection, indicating reliance on active adaptation rather than a static defense. The proposed Progressive Intensity Scheduling improves safety robustness on Qwen2.5 and Gemma-3 while reducing harmful trait expression.

arXiv cs.CR·Sep 9, 13:19 UTC · 7d agoAI safety & security1

Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness lets VLM agents control robots via discrete semantic action units, outperforming VLA baselines zero-shot and after light fine-tuning.

Show-Harness is an embodied agent harness that exposes discrete semantic action units a VLM reasons over, with embodiment-specific interpreters grounding them into local robot actions. It enables zero-shot robot control with closed-source frontier VLMs and low-cost adaptation of small open-source VLMs using only a few GPU-hours of fine-tuning. The companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection without specialized teleoperation hardware. Experiments show robust generalization across tasks, embodiments, and environments, beating representative agentic and VLA paradigms.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:53 UTC · 6d agoAI research

Can We Stop The Ads? Taxonomy and Characterization of Smartphone Splash Ads and Existing Countermeasures

Study of 108 ad-defense implementations finds only one tool blocked splash-ad navigation across ten popular apps, and it required Accessibility permission.

The paper taxonomizes smartphone splash ads — full-screen ads at app launch that trick users into trigger mechanisms such as moving the phone — and analyzes 108 documented advertising defenses for deployment barriers. Many defenses require device rooting, jailbreaking, runtime code injection, or application modification; others need extra permissions, rule maintenance, compilation, or payment. In evaluating 13 configurations of 11 tools across 10 popular apps, only one prevented ad-triggered navigation across all ten apps, requiring Accessibility permission and leaving ads visible roughly one second before dismissal. Documented harms include delayed emergency response, driver distraction, and degraded accessibility for vision-impaired users.

arXiv cs.CR·Sep 15, 15:24 UTC · 23h agoResearch

Chaotic Eclipse Released ShieldCrash, A PoC For Microsoft Defender Zero-Day

Researcher Chaotic Eclipse released ShieldCrash, a PoC showing Microsoft Defender's CVE-2026-69414 patch is incomplete, enabling arbitrary file reads as SYSTEM.

Security researcher Chaotic Eclipse published ShieldCrash, a proof-of-concept exploit for the Microsoft Malware Protection Engine privilege escalation vulnerability CVE-2026-69414 (ShieldBreak). The PoC performs arbitrary file reads with SYSTEM privileges on all supported Windows versions, including systems running the September 2026 security updates and Malware Protection Engine version 1.1.26080.3. The researcher claims Microsoft's patch fixed several exploit paths but missed a specific condition that still enables the attack. His recent releases also include zero-day PoCs targeting Nvidia, Kaspersky, Avast, and CrowdStrike Falcon.

Security Affairs·Sep 9, 07:53 UTC · 7d agoExploit / PoCCVE-2026-69414

Nightmare-Eclipse Strikes Again with 'ShieldCrash' Windows Exploit

A researcher known as Nightmare-Eclipse published another zero-day exploit, dubbed ShieldCrash, targeting Windows Defender.

Dark Reading reports that the disgruntled researcher tracked as Nightmare-Eclipse continued a vendetta against Microsoft by publishing a new zero-day exploit named ShieldCrash for Windows Defender. The brief report does not detail affected versions, exploitation prerequisites, or whether exploitation has been observed.

Dark Reading·Sep 10, 15:29 UTC · 5d agoExploit / PoC1

Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

Researchers release AssayBench-Loop, a 1,389-screen CRISPR benchmark, and AssayLoop, a framework that learns adaptive hit discovery policies.

The paper introduces AssayBench-Loop, a large-scale benchmark of 1,389 CRISPR screens across five phenotype categories for adaptive hit discovery under budget constraints. It also introduces AssayLoop, which combines AssayFormer, a transformer-based amortized acquisition policy trained across historical screens, with LLM-derived biological priors via an adaptive handoff. On temporally held-out screens, AssayLoop achieves 5.67-fold enrichment over random selection and recovers 27.7% of hits after assaying roughly 5% of the candidate library, outperforming existing adaptive-design methods and standalone LLMs.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 5d agoAI research1

Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails

Research shows on-policy expert correction, not imitation fine-tuning, lets weaker agent models catch up under evolved harnesses.

Researchers study how to combine automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks. Naively training weaker models (Qwen3-Coder, Gemma 4) on expert trajectories under an evolved harness regressed performance by 4 to 30 points on all tasks. They propose an on-policy correction pipeline, automated by a meta-level MLE agent, where an expert rewrites only the failing turn of the weaker model's rollout, preserving model-harness fit.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:53 UTC · 7d agoAI research

Indusface SwyftComply AI enables autonomous virtual patching for AI-discovered flaws

Help Net Security·Aug 4, 00:00 UTC · Aug 4, 2026Vulnerability

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent Space·Aug 22, 07:30 UTC · 25d agoAI tools & infra

Sevii APS Module preempts attacks with autonomous cyber defens

Help Net Security·Aug 4, 00:00 UTC · Aug 4, 2026Exploit / PoC

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models·Sep 4, 12:43 UTC · 12d agoModel release1

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement

Researchers propose ModularRSI, a modular benchmark-disjoint recursive self-improvement framework that evolves agent harnesses across five modules, improving TB2.0 and SWE-Bench Verified results.

ModularRSI targets generalizable recursive self-improvement (RSI) for agent harnesses by contrasting successful and failed trajectories for the same task and aggregating evidence across tasks to find recurring behavioral deficiencies. It decomposes the evolvable harness into five modules—Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection—each evolved independently within a restricted scope, then integrated with conflict resolution. Using 2,000 executable evolution tasks disjoint from evaluation benchmarks, it shows consistent gains on TB2.0 and SWE-Bench Verified and transfers across different foundation models.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

ShieldBreak: New Windows Zero-Day Bypasses Microsoft’s RoguePlanet Patch

Researcher Chaotic Eclipse published a PoC bypassing Microsoft's CVE-2026-50656 Defender patch, enabling SYSTEM-level code execution on fully patched Windows systems.

Security researcher Chaotic Eclipse released a proof-of-concept for ShieldBreak, a Microsoft Defender zero-day that fully bypasses the patch for CVE-2026-50656 (RoguePlanet), a race condition in the Malware Protection Engine (mpengine.dll) allowing SYSTEM-level code execution. The PoC was tested on Windows 11 25H2 and Windows Server 2025 with a 100% success rate, and Windows 10 is also vulnerable though not supported by the PoC; the flaw may also leak 8 bytes of memory. Microsoft's MSRC had criticized the researcher's repeated uncoordinated disclosures, including YellowKey (BitLocker bypass), GreenPlasma, and LegacyHive (User Profile Service LPE).

Security Affairs·Aug 12, 08:08 UTC · Aug 12, 2026Exploit / PoCCVE-2026-50656

Your Agent Aced the Task. Will It Do It Again?

IBM Research Hugging Face post examines whether LLM agents that succeed at a task once will reliably succeed again.

Hugging Face published an IBM Research blog post titled 'Your Agent Aced the Task. Will It Do It Again?' with URL slug 'altk-evolve-consistency'. No article text was provided, but it appears to address agent consistency and reliability evaluation across repeated task runs. This is relevant to developers building or evaluating LLM agent systems.

Hugging Face Blog·Sep 15, 16:00 UTC · 22h agoAI tools & infra

DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination

DeCAL, a contact-aware dexterous vision-language-action model with visuo-tactile fusion, reports 71% average task success.

DeCAL is a physically-grounded dexterous vision-language-action (VLA) model built on a Mixture-of-Transformers architecture with specialized experts for understanding, imagination, and action generation. It introduces Adaptive Visuo-Tactile Fusion with contact-aware gating and Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics. It reports state-of-the-art results with a 71% average success rate and 83.4% progress success rate, plus generalization to unseen scenarios.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:47 UTC · 7d agoAI research1

Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI safety & security1

ArmorCode enhances attack path analysis with new AI agents and Context Risk Graph

Help Net Security·Aug 5, 00:00 UTC · Aug 5, 2026Vulnerability

Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents

Duplex Cue evaluation shows PersonaPlex full-duplex agents adapt in-turn to listener contributions in only 34.8% of collaborative cases versus 68.2% for humans.

The paper introduces Duplex Cue, an evaluation of in-turn adaptation in full-duplex voice agents that separates listener intent (backchannel, collaboration, interruption) from speaker behavior (continue, adapt, yield). Using 208 scorable pairs from 300 human-confirmed cues in unscripted English conversations, it compares recorded human responses with PersonaPlex continuations generated while replaying listener audio. Humans adapt within the turn in 68.2% of collaborative pairs versus 34.8% for PersonaPlex, which otherwise continues unchanged (42.4%) or yields (22.7%).

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:46 UTC · 4d agoAI research

Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness enables VLM agents to control robots via a semantic action interface, achieving zero-shot frontier control and few-GPU-hour adaptation of small VLMs.

Show-Harness exposes discrete semantic action units that VLMs reason over, with embodiment-specific interpreters deterministically grounding them into local robot actions. It enables zero-shot closed-source frontier VLM control and adapts small open-source VLMs for low-cost deployment with a few GPU-hours of fine-tuning. The companion GUMI interface extends the same semantic action space to GUI-based demonstration collection without teleoperation hardware, and Show-Harness-equipped agents outperform representative agentic and VLA paradigms.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

Windows Defender ShieldCrash 0-Day Lets Attackers Read Arbitrary Files as SYSTEM

Unpatched Windows Defender zero-day 'ShieldCrash' PoC lets local attackers read arbitrary files as SYSTEM, apparently bypassing the CVE-2026-69414 patch.

Researcher MSNightmare published a skeleton proof-of-concept for 'ShieldCrash', an unpatched Microsoft Defender flaw enabling arbitrary file reads with SYSTEM privileges that reportedly persists on supported Windows versions after September 2026 updates. It appears to bypass the recent fix for the Malware Protection Engine elevation-of-privilege flaw CVE-2026-69414 (ShieldBreak), patched in engine version 1.1.26080.3. SYSTEM-level file disclosure could expose credentials, application secrets and registry hives and support post-compromise reconnaissance. No active exploitation is confirmed and Microsoft had issued no specific patch or mitigation at disclosure time.

GBHackers·Sep 9, 08:40 UTC · 7d agoExploit / PoCCVE-2026-69414

What Fal.Con 2026 Reinforced: AI Makes Proving Exposure More Important Than Ever

Horizon3's Fal.Con 2026 recap argues AI-accelerated vulnerability discovery makes continuous attacker-based exposure validation essential for defenders.

In a Fal.Con 2026 recap, Horizon3 argues that AI is compressing the time between vulnerability discovery and exploitation, making attacker-derived evidence about real exploitability the key prioritization signal. Horizon3 announced it joined CrowdStrike's Project QuiltWorks, with NodeZero exploitability intelligence flowing into Falcon Next-Gen SIEM and Falcon Fusion SOAR workflows able to trigger NodeZero 1-Click Verify for remediation testing. The company reported running over 1,200 NodeZero demos during the show, and CrowdStrike CEO George Kurtz's keynote framed AI red teaming and offense-informing-defense as central themes.

Horizon3.ai·Sep 4, 17:46 UTC · 11d agoIndustry