All news with #ai security tag

🔍 Bitdefender is hosting a webinar to separate speculative cybersecurity headlines from evidence-based risks organizations should prioritize for 2026. The session centers on three converging trends: ransomware evolving into targeted disruption, uncontrolled internal AI adoption that erodes perimeter assumptions, and a sober assessment of claims about AI-orchestrated adaptive attacks. Attendees receive research-backed guidance to align investments and defenses with real operational risk.

🔐 AI tool poisoning is an attack where malicious instructions are embedded in tool descriptions used by AI agents, causing the agent to exfiltrate data or perform unauthorized actions. The blog explains how attacks — including hidden instructions, misleading examples, and permissive schemas — exploit agent interpretation of tool metadata. It recommends runtime monitoring, description validation, input sanitization, and strict identity and access controls to reduce risk.

📝 Google is introducing an AI Inbox in Gmail that uses Gemini to surface prioritized briefings and summarize conversation threads with new AI Overviews. The feature appears above the traditional Inbox as a personalized briefing that highlights to-dos and VIP contacts inferred from email signals. AI Overviews are rolling out to all users at no cost, while AI Inbox is available to Google AI Pro and Ultra subscribers in the United States. Google says users can opt out and reiterated it will not train its AI models on user emails.

🛡️ AI and large language models are transforming cybersecurity into a contest of speed and scale, serving as both best-in-class defensive tools and powerful offensive enablers. Researchers describe self-modifying malware and autonomous espionage that call commercial LLMs (e.g., PROMPTFLUX, PROMPTSTEAL) to adapt tactics mid-execution, while defenders are deploying solutions like XBOW, CodeMender and Watsonx to automate vulnerability discovery, remediation and compliance. CISOs must therefore pair AI-driven defenses with governance and model guardrails to manage this dual-use reality.

🔐 Corelight outlines six cyber threats to prioritize in 2026, driven by advances in AI, automation, and more sophisticated social engineering. Key concerns include agentic and shadow AI misuse, deepfakes in phishing, AI-orchestrated ransomware, accelerated vulnerability discovery, stale scanning practices, and multicloud blind spots. Recommendations focus on improved hybrid visibility, continuous scanning, Zero Trust access, digital identity verification, and deploying NDR alongside AI-enabled incident response to reduce detection gaps.

🔒 AI systems face a growing range of attacks—from data poisoning and model poisoning during training to adversarial inputs, prompt injection, and model theft during deployment. These threats exploit weak data governance, supply chain dependencies, and inadequate monitoring. Security leaders should adopt proactive controls including provenance tracking, adversarial testing, rate limits, and routine red teaming. Frameworks like MITRE ATLAS can help map attacker techniques and prioritize defenses.

🧠 Across dark web forums, Telegram channels, and underground marketplaces, criminals are framing AI as a shortcut to profit rather than a technical revolution. The rise of "vibe hacking" — an intuition-driven, AI-guided approach — and branded tools like FraudGPT, PhishGPT, and WormGPT lower the skill barrier and package familiar scams as turnkey services. AI jailbreaking, prompt-injection techniques, and "Hacking-GPT" offerings are openly bought and sold, amplifying volume over sophistication. Flare monitors those signals to give defenders earlier visibility.

🔒 Researchers propose a defensive data-poisoning tool called AURA to protect proprietary knowledge graphs that feed LLMs. The method injects plausible but false entries that authorized users can filter out with a secret key, while stolen graphs become unreliable for attackers. The authors report degrading unauthorized accuracy to 5.3% and preserving 100% fidelity for key-holders with under 14% max latency overhead.

🔍 This special report helps IT leaders align near-term planning with 2026 priorities by emphasizing greater agility, flexibility, and measurable business outcomes. It stresses the need to automate, streamline, and modernize IT operations to counter skills shortages and meet rising demand. Four feature pieces examine strategy beyond AI, the cost of cloud fragility, how AI agents reshape supply chains, and AI's implications for cybersecurity.

🔐 Generative AI is accelerating password attacks against Active Directory, making cracking cheaper, faster, and more targeted than traditional techniques. Models like PassGAN learn real-world password patterns and can predict employee passwords when trained on breach data or public company content. Combined with readily available GPU cloud rentals, attackers can test vastly more candidates and tailor guesses using org-specific reconnaissance. Vendors such as Specops recommend longer, random passphrases and breached-password screening to reduce exposure.

🔍 Microsoft pushed back after security engineer John Russell disclosed multiple prompt injection and sandbox-related issues in Copilot, which the company says do not meet its vulnerability criteria. Russell reported indirect and direct prompt injection that could leak the system prompt, a file-upload bypass via base64-encoding, and the execution of commands inside Copilot's isolated Linux environment. Microsoft told BleepingComputer it reviewed the reports against its public bug bar and assessed them as out of scope when they did not cross clear security boundaries or impacted only the requesting user's environment. The exchange highlights differing definitions of AI risk between vendors and researchers.

🔒 Palo Alto Networks announced that Prisma AIRS, accelerated on the NVIDIA BlueField DPU, is now part of the NVIDIA Enterprise AI Factory validated design. The integration embeds zero trust runtime security into AI infrastructure by running Prisma AIRS Network Intercept on BlueField and extending enforcement to cloud environments. It leverages NVIDIA DOCA and DOCA Argus telemetry to feed Cortex XSIAM and Cortex XSOAR for AI-driven detection and response, and recommends hyperscale firewall clusters for defense-in-depth and improved TCO.

🔒 Check Point and NVIDIA announced an integrated security capability to protect AI "factories" across the entire AI lifecycle, from data ingestion and model training to deployment and inference. The effort targets growing risks such as prompt manipulation and attacks on GenAI infrastructure, which Gartner and other industry surveys identify as rising threats. The collaboration focuses on unified visibility, real-time detection, runtime protection, and centralized policy enforcement to reduce operational risk and help organizations meet compliance and governance requirements.

👁 Flock’s exposed livestreams show that its AI-enabled Condor pan-tilt-zoom (PTZ) cameras can automatically zoom in on and track people in public spaces. Reporters observed high-resolution footage capturing individuals on bike paths, in parking lots, at playgrounds, and at stoplights, with cameras following faces and recording close-up detail. These exposures underscore privacy and security risks from networked AI surveillance and inadequate access controls.

🔒 2025 saw a convergence of large-scale breaches, state-aligned intrusions, and rapidly maturing AI-enabled attacks that reshaped the threat landscape. High-profile incidents included the ByBit $1.5B Ethereum heist, Clop exploitation of Oracle zero-days, and mass data-theft campaigns targeting Salesforce and adult platforms. Attackers amplified impact with terabit-scale DDoS, developer supply-chain abuse, and social-engineering techniques such as ClickFix and help-desk compromises. Organizations raced to patch zero-days, lock down developer pipelines, and defend against AI-powered malware and novel prompt-injection vectors.

🔒 Many SOC teams are experimenting with AI but fail to operationalize it, treating models as shortcuts for broken processes rather than engineering solutions. Christopher Crowley summarizes 2025 SANS SOC findings and identifies five practical SOC workflows—detection engineering, threat hunting, software development, automation, and reporting—where narrowly scoped, testable AI can add reliable value. He stresses rigorous validation, human accountability, and ongoing tuning to avoid overreliance on out-of-the-box models.

🛡️ Cybersecurity frameworks require ongoing reassessment; this article highlights seven warning signs that your program may need substantial revision. Industry experts recommend adopting a dynamic detection-and-response model, integrating AI, and aligning frameworks to NIST while avoiding purely compliance-driven designs. Common problems include failing continuous monitoring, reactive alert triage, declining KRIs/KPIs, and recent incidents. Practical advice: schedule structured reviews, add interim check-ins, and rebuild when incremental fixes no longer suffice.

🛡️ OWASP published the Agentic Applications Top 10 for 2026 to classify risks unique to autonomous AI agents. Koi Security summarizes multiple real incidents from the past year — malicious MCP servers, poisoned assistants, and RCEs in Claude Desktop extensions — that show how autonomy expands attack surfaces. The report stresses inventorying runtime dependencies, enforcing least privilege, and monitoring agent behavior to detect and contain attacks.

🔒 Check Point announced multiple 2025 recognitions from leading analyst firms and independent research labs, underscoring its focus on securing AI-driven environments and distributed networks. The company emphasized a prevention-first philosophy that unifies security management and strengthens Zero Trust frameworks. These honors reflect validation of its strategy to enable safe enterprise AI adoption amid growing cyber threats.

🔒 2025 exposed major, real-world risks across the AI ecosystem as rapid adoption of agentic AI expanded enterprise attack surfaces. Researchers documented pervasive Shadow AI and vulnerable vendor tools, AI supply-chain poisoning, credential theft (LLMjacking), prompt-injection attacks, and rogue or misconfigured MCP servers. These incidents affected popular frameworks and cloud services and resulted in data breaches, remote-code execution, and costly fraud.