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AWS puts AI vulnerability detection to the test, and false positives pile up
AWS publicly released its Deception Benchmark (14,822 samples) showing leading AI models falsely flag 41-99% of safe code as vulnerable.
AWS released its Deception Benchmark publicly, containing 14,822 samples across 16 programming languages and more than 70 CWE categories, with 9,695 scored samples split into 6,988 code-level and 2,707 environment-gated challenges. AWS evaluated 12 models from five providers using single-turn prompts and found none met its production bar of below 10% for both false-positive and false-negative rates. With direct prompting, models caught nearly all real vulnerabilities but incorrectly flagged 41% to 99% of safe code, with precision between 52% and 71%. Asking models to prove exploitability reduced false positives by 17 to 74 percentage points but raised false-negative rates to 7-44%, with models struggling most when external controls like Kubernetes Network Policies blocked apparent exploits.
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OpenAI Builds ‘Defense Factory’ as AI Agents Gain Ability to Chain Cyber Exploits
OpenAI unveiled a Defense Factory using AI agents to continuously discover, validate, patch, and verify vulnerabilities, warning the defender's window against agentic attackers is shrinking.
OpenAI describes a Defense Factory workflow where AI agents integrate source control, scanners, issue trackers, and secret stores to discover, reproduce, patch, and verify vulnerabilities under human oversight. The approach responds to agentic attackers that can retain knowledge across sessions and chain vulnerabilities into multi-stage attack paths faster than human triage can respond, which OpenAI calls a shrinking defender's window. During an internal security sprint involving 250+ people across 100+ service areas, agents closed 53 urgent or high-priority issues on day one, achieved 90.6% ownership-routing acceptance, cut 37% of findings as duplicates, and produced Codex-generated patches with a 0.53% rollback rate. Runtime validation reduced false positives to 0.81%, and each agent operates in isolated, reproducible environments with a control plane for policy and credentials.
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How to Keep Malware’s Rotating Infrastructure From Becoming a Detection Gap
ANY.RUN marketing piece argues SOC detection lags rotating malware and phishing infrastructure, citing a 46-country campaign and 3DBlast kit to promote TI feeds.
ANY.RUN describes how malware and phishing campaigns rotate domains and hosting, making single-IOC blocking ineffective for SOC teams. One investigated phishing campaign spanned 46 countries with 425 kit URLs across 240 hosts, 94% of which were seen for only a single day. A phishing kit dubbed 3DBlast impersonates Microsoft 365 and Google using BitB, AiTM, OAuth device-code phishing and DOM relay techniques. The article is primarily a promotion for ANY.RUN TI Feeds and TI Lookup products.
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SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.
SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.
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PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift
PIDS-Bench shows prompt-injection detectors scoring F1 above 0.98 still misclassify about one-third of external benign security-adjacent prompts, revealing provenance-sensitive over-defense.
PIDS-Bench is a frozen multi-axis benchmark that jointly evaluates prompt-injection detectors on attack detection and benign false-positive behavior at fixed thresholds, spanning in-distribution inputs, hard-benign prompts, obfuscated attacks, and domain/structural distribution shifts. It evaluates seven detectors plus a rule-based lower-bound reference. A detector exceeding F1 = 0.98 on held-out data still misclassifies roughly one-third of an externally-sourced benign security-adjacent subset, and no internal detector reaches F1 >= 0.95 with hard-benign FPR <= 0.10 on the stress distribution. Hard-negative augmentation nearly eliminates over-defense on curated stress inputs but leaves it intact on externally-sourced prompts, a pattern termed provenance-sensitive over-defense.
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When the Whole Company Adopts AI: What It Does to Your SOC
Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.
A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.
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12 Best Ransomware Protection Solutions Compared (2026): Features & Pricing
GBHackers compares 12 ransomware protection solutions for 2026, recommending layered stacks of EDR prevention, managed detection, containment, and guaranteed recovery.
The editorial comparison argues no single product stops ransomware, recommending a layered strategy across prevention, early detection, blast-radius containment, and clean recovery. CrowdStrike and SentinelOne are named best-in-class EDR, Huntress and Sophos MDR for managed 24/7 coverage, ColorTokens for microsegmentation containment, and Rubrik and Acronis for guaranteed recovery. The guide notes ransomware is now professionalized with double extortion and encryption sprints measured in minutes.
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Predicting Privacy Leakage from Weight Spectral Density
Study shows WeightWatcher spectral metrics like stable rank correlate with membership inference vulnerability, enabling cheaper ML privacy auditing.
The paper tests whether spectral metrics from the heavy-tailed self-regularisation framework can proxy membership inference attack (MIA) vulnerability without training expensive shadow models. On image and tabular classification tasks, stable rank correlates positively with overall MIA success, while Log alpha-Norm correlates negatively at the low false-positive regime. These correlations are stronger than those obtained from the generalisation gap, suggesting weight spectra capture leakage information overfitting measures miss. The authors propose spectral analysis as a scalable direction for privacy auditing.
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OpenAI Builds ‘Defense Factory’ Where AI Agents Continuously Find and Fix Vulnerabilities
OpenAI unveils a Defense Factory where AI agents continuously discover, validate, and fix vulnerabilities, integrating GitHub, Snyk, Semgrep, Tenable, and ServiceNow.
OpenAI introduced a Defense Factory, an agent-first cybersecurity operation that connects AI agents to developer and security tools via APIs, CLIs, and Model Context Protocol integrations including GitHub, GitLab, Snyk, Semgrep, Tenable, Jira, Linear, and ServiceNow. During an internal security sprint, over 250 people across more than 100 service areas closed 53 urgent or high-priority issues on day one, achieved a 90.6% accepted ownership-assignment rate, and Codex generated all remediation patches with only 0.53% rolled back. Agent-assisted deduplication flagged 37% of findings as duplicates, and runtime validation reproduced 19.5% of findings, cutting the false-positive rate to 0.81%. OpenAI argues defenders must exploit a temporary 'defender's window' using source-code access and frontier models before open-weight models enable autonomous offensive agent fleets.
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Product showcase: GitGuardian Honeytoken catches credential theft as it happens
GitGuardian now deploys honeytoken decoy AWS credentials across developer fleets via MDM, generating high-fidelity alerts when infostealers test stolen credentials.
GitGuardian extended its honeytoken platform so decoy AWS credentials can deploy fleet-wide to developer laptops through existing MDM tooling, with deleted decoys automatically replaced at the next sync. When a harvester tests a decoy, the platform logs the API call in the controlling AWS account and raises an incident naming the source machine and file, routed via email, Slack, Teams, ServiceNow, or webhooks. The capability responds to infostealers such as Shai-Hulud that scan entire filesystems and validate stolen credentials within seconds. GitGuardian is building decoys for Kubernetes and package registry credentials using random, structure-free identifiers that resist detection.
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Mars Security Debuts Automated Threat Engine Processing Live Cyber Intelligence Into Validated Rules Within Minutes
Mars Security launches Real-Time Intel-Based Detection, converting threat intelligence advisories into validated, ATT&CK-mapped detection rules within minutes for SOCs.
Mars Security, an autonomous threat hunting and detection engineering platform founded by former offensive security operators, announced Real-Time Intel-Based Detection. The capability ingests advisories from sources like CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence, maps indicators to MITRE ATT&CK, and authors native query logic across connected infrastructure including CrowdStrike Falcon, Wiz, Splunk, Sysmon, identity providers, Snowflake, and Databricks. Every rule is backtested against 30 days of historical telemetry to quantify false positives before analyst approval and one-click deployment. The feature is free for existing customers and available via AWS Marketplace.
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Mars Security brings threat intelligence to detection in real time
Mars Security launched Real-Time Intel-Based Detection, converting advisories from CISA and Mandiant into backtested MITRE ATT&CK-mapped detection rules for CrowdStrike, Wiz, and Splunk.
Mars Security announced a capability that automatically turns newly published threat intelligence from sources like CISA, Mandiant, Unit 42, and Microsoft into MITRE ATT&CK-mapped detection rules. Each rule is written in the native query language of the customer's telemetry (CrowdStrike Falcon, Wiz, Splunk, firewalls, identity providers, AWS, Snowflake, Databricks) and backtested against 30 days of the customer's data before deployment, with indicator scoring to drop noisy or stale indicators. The platform also maps existing detection coverage, flags gaps such as AWS CloudTrail tampering and pass-the-hash movement, and delivers some recommendations as open pull requests for detection-as-code workflows.
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Mars Security Launches Real-Time Intel-to-Detection Engine That Turns Live Threat Intelligence Into Backtested Detections in Minutes
Mars Security launched Real-Time Intel-Based Detection, converting advisories into MITRE ATT&CK-mapped, backtested detection rules for CrowdStrike, Wiz, and Splunk within minutes.
The capability turns newly published threat intelligence from CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence into validated detection rules within minutes, each backtested against 30 days of the customer's own telemetry before deployment. Rules are written in native query languages across CrowdStrike Falcon, Wiz, Splunk, firewalls, Linux Sysmon, identity providers, AWS telemetry, and data lakes such as Snowflake and Databricks, with no data ingestion or stack changes. The feature is available at no additional cost to existing customers and on AWS Marketplace. Mars also flags detection coverage gaps and extends monitoring to credentials leaked by AI coding agents.
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FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience
FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.
FlowBalance calibrates dense self-guidance scores with verifier-derived group advantages: guidance is retained on positive-advantage trajectories, reversed on negative-advantage trajectories, and disabled when rollout groups show no outcome preference. The method exponentially reweights a reference policy via trajectory balance, with guarantees including within-group contrast preservation and a minimum-change reverse-KL characterization. On mathematical reasoning it outperforms FlowRL on Qwen3-4B and Qwen3-8B, trains faster and more stably, avoids direct OPSD's response-length collapse, and shows higher correct-strategy diversity on AIME24.
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[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens
Anthropic launched Claude Fable 5.1 and Mythos 5.1, claiming new SOTA benchmarks, with 75% cache-read price cut and 1M-token context.
Anthropic released Claude Fable 5.1 and Mythos 5.1 as flagship models for coding and knowledge work, with a 1M-token context window and pricing of $10/$50 per million input/output tokens and cache reads cut 75% to $0.25. Artificial Analysis Intelligence Index scored Fable 5.1 at 66 versus 63 for Claude Opus 5, with HLE at 59.1% and Terminal-Bench v2.1 at 91.4%, though per-task cost rose ~20% due to 1.7x output token usage. Community analysis suggested Fable and Mythos may share underlying weights with different safety/routing behavior, and release notes highlighted Enterprise Frontier Safeguards and zero-data-retention support.
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CISA Red Team Compromised Two Critical Infrastructure Orgs, One Detected Nothing
CISA red teams fully compromised two critical infrastructure orgs with similar tradecraft; one detected nothing, the other contained phishing within minutes.
CISA advisory AA26-237A, 'A Tale of Two SOCs,' describes two simultaneous red team assessments of a Government Services organization and a Water and Wastewater Systems organization, both compromised to the domain level. Organization A never detected the intrusion, enabled by default credentials, a misconfigured AD CS template (ESC1), cleartext credentials, non-expiring static AWS keys, and over-permissioned Entra ID applications. Organization B detected and isolated phishing within 2-20 minutes, though the red team still reached krbtgt via DCSync from cleartext SCCM credentials. CISA attributes the outcome gap to people and processes rather than the security tools themselves.
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Cloudflare Workers Spectre Attack Leaks JWT From Co
Researchers demonstrated a remote Spectre attack leaking a JWT between co-located Cloudflare Workers at 12 bits per second, 360x the 2021 rate.
Security researchers showed a remote Spectre attack against Cloudflare Workers that leaked a JWT from a co-located Worker at up to 12 bits per second with 99.16% accuracy, 360 times the 2021 demonstration rate. The attack exploited weaknesses in DyPrIs dynamic process isolation, where long-lived Durable Object invocations could run before isolation and WebSocket-heavy I/O suppressed the branch-misprediction signal. Cloudflare deployed improved DyPrIs, the V8 Sandbox, and MPK-based in-process isolation, and found no indicators of active exploitation over three years.
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