All news with #ai security tag

🛡️ In just a few years, AI has shifted from futuristic innovation to core business infrastructure, yet security practices have not kept pace. Palo Alto Networks presents a Secure AI by Design Policy Roadmap that defines the AI attack surface and prescribes actionable measures across external tools, agents, applications, and infrastructure. The Roadmap aligns with recent U.S. policy moves — including the June 2025 Executive Order and the July 2025 White House AI Action Plan — and calls for purpose-built defenses rather than retrofitting legacy controls.

🔒 Google on Tuesday introduced Private AI Compute, a cloud privacy capability that aims to deliver on-device-level assurances while harnessing the scale of Gemini models. The service uses Trillium TPUs and Titanium Intelligence Enclaves (TIE) and relies on an AMD-based Trusted Execution Environment to encrypt and isolate memory on trusted nodes. Workloads are mutually attested, cryptographically validated, and ephemeral so inputs and inferences are discarded after each session, with Google stating data remains private to the user — 'not even Google.' An external assessment by NCC Group flagged a low-risk timing side channel in the IP-blinding relay and three attestation implementation issues that Google is mitigating.

🔐 DDI logs — DNS, DHCP and IP address management — are the authoritative record of network behavior, and when combined with AI become a high-fidelity source for threat detection and automated response. Integrated DDI-AI correlates disparate events into actionable incidents, enabling SOAR-driven quarantines and DNS blocking at machine speed. This fusion also powers continuous, AI-driven breach and attack simulation to validate defenses and harden models.

🔦 Shadow AI — the unsanctioned use of generative and agentic tools by employees — is creating a sizeable security blind spot for IT teams. Unsanctioned chatbots, browser extensions and autonomous agents can expose sensitive data, introduce vulnerabilities, or execute unauthorized actions. Organizations should inventory use, define realistic acceptable-use policies, vet vendors and combine technical controls with user education to reduce data leakage and compliance risk.

🔒 A Wiz analysis of 50 private companies from the Forbes AI 50 found that 65% had exposed verified secrets such as API keys, tokens and credentials across GitHub and related repositories. Researchers employed a Depth, Perimeter and Coverage approach to examine commit histories, deleted forks, gists and contributors' personal repos, revealing secrets standard scanners often miss. Affected firms are collectively valued at over $400bn.

🔎 Microsoft researchers disclosed a new side-channel attack called Whisper Leak that can infer the topic of encrypted conversations with language models by observing network metadata such as packet sizes and timings. The technique exploits streaming LLM responses that emit tokens incrementally, leaking size and timing patterns even under TLS. Vendors including OpenAI, Microsoft Azure, and Mistral implemented mitigations such as random-length padding and obfuscation parameters to reduce the effectiveness of the attack.

🔒 Researchers at Tenable identified seven techniques that can coerce ChatGPT into disclosing private chat history by abusing built-in features like web browsing and long-term Memories. They show how OpenAI’s browsing pipeline routes pages through a weaker intermediary model, SearchGPT, which can be prompt-injected and then used to seed malicious instructions back into ChatGPT. Proof-of-concepts include exfiltration via Bing-tracked URLs, Markdown image loading, and a rendering quirk, and Tenable says some issues remain despite reported fixes.

🔒 Microsoft has disclosed a novel side-channel called Whisper Leak that can let a passive observer infer the topic of conversations with streaming language models by analyzing encrypted packet sizes and timings. Researchers at Microsoft (Bar Or, McDonald and the Defender team) demonstrate classifiers that distinguish targeted topics from background traffic with high accuracy across vendors including OpenAI, Mistral and xAI. Providers have deployed mitigations such as random-length response padding; Microsoft recommends avoiding sensitive topics on untrusted networks, using VPNs, or preferring non-streaming models and providers that implemented fixes.

🔍 Microsoft researchers disclosed "Whisper Leak", a new side-channel that can infer conversation topics from encrypted, streamed language model responses by analyzing packet sizes and timings. The study demonstrates high classifier accuracy on a proof-of-concept sensitive topic and shows risk increases with more training data or repeated interactions. Industry partners including OpenAI, Mistral, Microsoft Azure, and xAI implemented streaming obfuscation mitigations that Microsoft validated as substantially reducing practical risk.

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

🔍 AI can now produce highly convincing receipts that reproduce paper texture, detailed itemization, and forged signatures, making manual review unreliable. Expense platforms and employers are deploying AI-driven detectors that analyze image metadata and transactional patterns to flag likely fakes. Simple countermeasures—users photographing or screenshotting generated images to remove provenance data—undermine those checks, so vendors also examine contextual signals like repeated server names, timing anomalies, and broader travel details, fueling an ongoing security arms race.

🔒 Check Point expands CloudGuard to protect GenAI applications by extending the ML-driven, open-source CloudGuard WAF that learns from live traffic. The platform moves beyond traditional static WAFs to secure web interactions, APIs (REST, GraphQL) and model-integrated endpoints with continuous learning and high threat-prevention accuracy. This evolution targets modern attack surfaces introduced by generative AI workloads and APIs.

🚀 LMCache implements a node-local, tiered KV Cache on GKE to extend the GPU HBM-backed Key-Value store into CPU RAM and local SSD, increasing effective cache capacity and hit ratio. In benchmarks using Llama-3.3-70B-Instruct on an A3 mega instance (8×nvidia-h100-mega-80gb), configurations that added RAM and SSD reduced Time-to-First-Token and materially increased token throughput for long system prompts. The results demonstrate a practical approach to scale context windows while balancing cost and latency on GKE.

🔐 Enterprises must redesign security as AI moves from experimentation to production, and CIOs need a prevention-first, unified approach. This guide reframes Confidentiality, Integrity and Availability for AI, stressing rigorous access controls, end-to-end data lineage, adversarial testing and a defensible supply chain to prevent poisoning, prompt injection and model hijacking. Palo Alto Networks advocates embedding security across MLOps, real-time visibility of models and agents, and executive accountability to eliminate shadow AI and ensure resilient, auditable AI deployments.

🔎VirusTotal ran VT Code Insight, an AI-based Mach-O analysis pipeline against nearly 10,000 first-seen Apple binaries in a 24-hour stress test. By pruning binaries with Binary Ninja HLIL into a distilled representation that fits a large LLM context (Gemini), the system produces single-call, analyst-style summaries from raw files with no metadata. Code Insight flagged 164 samples as malicious versus 67 by traditional AV, surfacing zero-detection macOS and iOS threats while also reducing false positives.

🔍 Check Point Research and External Risk Management experts explain how combining AI-driven analytics with seasoned human threat hunters enables organizations to detect and anticipate attacks before they strike. The AMA webinar, featuring leaders like Sergey Shykevich and Pedro Drimel Neto, detailed telemetry fusion, rapid malware analysis, and automated triage to act at machine speed. Speakers stressed continuous intelligence, cross-team collaboration, and proactive hunting to shorten dwell time. The approach blends scalable automation with human context to prevent large-scale incidents.

🔐 This post demonstrates a proof-of-concept for teaching autonomous agents internet safety by integrating real-time threat intelligence. Using LangChain with OpenAI and the Cisco Umbrella API, the example shows how an agent can extract domains and query dispositions to decide whether to connect. The agent returns clear disposition reports and abstains when no domains are present. The approach emphasizes decision-making over hardblocking.

🛡️ Google Threat Intelligence Group (GTIG) reports that cybercriminals are actively integrating large language models into malware campaigns, moving beyond mere tooling to generate, obfuscate, and adapt malicious code. GTIG documents new families — including PROMPTSTEAL, PROMPTFLUX, FRUITSHELL, and PROMPTLOCK — that query commercial APIs to produce or rewrite payloads and evade detection. Researchers also note attackers use social‑engineering prompts to trick LLMs into revealing sensitive guidance and that underground marketplaces increasingly offer AI-enabled “malware-as-a-service,” lowering the bar for less skilled threat actors.

🔒 The article argues that AI-driven digital health initiatives proved essential during COVID-19 but simultaneously exposed critical cybersecurity gaps that threaten pandemic preparedness. It warns that expansive data ecosystems, IoT devices and cloud pipelines multiply attack surfaces and that subtle AI-specific threats — including data poisoning, model inversion and adversarial inputs — can undermine public-health decisions. The author urges security by design, including zero-trust architectures, data provenance, encryption, model governance and cross-disciplinary drills so AI can deliver trustworthy, resilient public health systems.

🔒 Check Point Software achieved the highest security effectiveness rating in the recent NSS Labs Enterprise Firewall Test, posting a 99.59% score. The result spotlights its prevention-first architecture and comprehensive threat coverage, which the company says outperformed competing vendors. The blog links this independent validation to rising AI-driven risks, citing Check Point Research findings that 1 in 54 GenAI prompts carries a high risk of sensitive-data exposure and that 91% of frequent AI users are affected, underscoring the need for robust network defense.