Search: “interpretability”

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CategoryAll AI safety & security 19 AI research 16 Malware 7 Research 6 AI industry 5 Model release 4 Vulnerability 4 Phishing & fraud 4 Industry 3 Data breach 3 Threat actor 3 AI tools & infra 2 Tools 2 Policy & legal 1

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Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security LabsSep 11, 00:00 UTC · 6d agoResearch1

Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

ExCYDER framework self-verifies anomaly detection alerts for DER power grids using LightGBM and SHAP, reaching over 98% detection accuracy.

The paper presents ExCYDER, an explainable AI anomaly detection framework for Distributed Energy Resource networks that combines LightGBM with SHAP to validate whether each model decision aligns with its feature-attribution evidence. On a realistic DNP3 dataset it achieved over 98% detection accuracy, 14.5 ms SHAP latency per alert, and confidence deviation within 5%. The self-verifying mechanism distinguishes coherent from inconsistent alerts, improving interpretability and auditability for DER-focused security operations centers.

arXiv cs.CRSep 11, 00:19 UTC · 6d agoResearch

Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42Sep 14, 10:00 UTC · 2d agoResearch

Hackers Stole Flock’s Camera Software, Revealing How the Company Tracks Cars and People

Hackers who removed a Flock Safety license plate camera dumped its data, revealing person-detection capabilities and an encryption key stored unencrypted on the device.

A hacker collective calling itself stegan0gram physically removed a Flock Safety automatic license plate reader camera from a roadway, copied its storage, and shared the files with 404 Media, WIRED, and Distributed Denial of Secrets. Analysis found an encryption key in an unencrypted 'media' partition that unlocked videos of thousands of vehicle detections, with logs showing more than a million images generated in weeks. The software explicitly detects people, bicycles, and even bumper stickers, and records from one Georgia city were searchable by more than 2,000 agencies nationwide. The findings follow 2025 research by Jon Gaines documenting flaws enabling root-level access to Flock cameras.

404 Mediaupdated 09:43 UTC · 11m agofirst Sep 16, 10:30 UTC · 23h agoResearch in the wild 4 sources

Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · securitySep 16, 02:07 UTC · 1d agoResearch HN 25↑ · 18 comments

Understanding the Usability of Cryptographic Verification Tools

Survey of Tamarin and ProVerif users reveals usability barriers: debugging non-termination, model validation, and opaque proof failures hinder cryptographic protocol verification.

The paper presents an exploratory human-centered survey of researchers, graduate students, and practitioners with hands-on experience using Tamarin, ProVerif, and related cryptographic protocol verification tools. Findings reveal usability barriers across the verification workflow, including difficulties debugging non-termination and performance issues plus the lack of systematic methods for validating formal models against real protocols. When proofs fail without concrete attacks, users commonly simplify models, add helper lemmas, and revisit modeling abstractions. Participants called for actionable diagnostics, clearer explanations of results, visualization, and automation for recurring proof tasks.

arXiv cs.CRSep 14, 20:40 UTC · 2d agoResearch