All news in category “AI and Security Pulse”

🛡️ Vibe coding accelerates development but often omits essential security controls, introducing vulnerabilities, data exfiltration, and destructive actions. Unit 42 documents incidents where AI-generated code bypassed authentication, executed arbitrary commands, deleted production databases, or exposed sensitive identifiers. To mitigate these risks, Unit 42 proposes the SHIELD framework—Separation, Human review, Input/output validation, Enforcer helper models, Least agency, and Defensive controls. Implementing these measures restores governance and enables safer AI-assisted development.

🔒 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.

🩺 OpenAI announced ChatGPT Health, a sandboxed space that lets users discuss health topics and optionally connect medical records and popular wellness apps (Apple Health, Function, MyFitnessPal, Weight Watchers, AllTrails, Instacart, Peloton) for tailored responses, lab-test insights, nutrition advice, meal ideas and suggested workouts. The feature is rolling out to Free, Go, Plus and Pro users outside the EEA, Switzerland and the U.K., and OpenAI says it is designed to support medical care, not replace diagnosis or treatment. Health operates in a silo with purpose-built encryption and isolation; conversations are not used to train OpenAI's foundation models, and connected apps require explicit permission and additional security review.

🔒 OpenAI has introduced ChatGPT Health, a private space for health conversations, and says by default it will not use your health information to train its foundation models. An in-dashboard alert observed during early-access testing states health data is subject to a Health Privacy Notice and recommends enabling multi-factor authentication. OpenAI cautions that ChatGPT is not a substitute for professional medical advice and notes the feature is rolling out to most users but is not yet available in the EEA, Switzerland, or the UK.

🧠 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.

🔍 As organizations embed AI and distribute workloads across cloud and edge environments, traditional security tooling increasingly misses hidden misconfigurations, inconsistent controls, and emergent AI-agent behaviors. Experts advise moving from reactive, tool-stacked approaches to a unified visibility strategy that normalizes telemetry, aligns people/processes/data, and continuously evaluates agentic behavior. Practical steps include using existing FinOps metrics, tagging, and cross-team audits to reveal anomalies, and applying AI-driven automation to integrate and extend current investments. A modern CMDB and enterprise knowledge graphs provide the contextual backbone needed for AI to correlate signals and surface risk without expanding the security stack.

🛡️ The growing use of generative AI in the workplace is raising security concerns as many employees access tools via personal accounts. Netskope's 2026 Cloud and Threat Report found 47% of workplace generative AI usage occurs through personal ChatGPT, Google Gemini or Microsoft Copilot accounts, reducing visibility and controls. Reported data-policy violations tied to LLMs have doubled, averaging 223 incidents per month and involving sensitive source code, intellectual property and credentials. Organizations are starting to curb Shadow AI use, but the report warns that stronger governance and employee education remain essential.

🛡️ Non-human employees — bots, AI agents, service accounts and automation scripts — are expanding enterprise attack surfaces as organizations scale AI and cloud automation. NHIs often live outside traditional IAM and frequently hold over-permissioned standing access and static credentials, making them attractive targets. The article recommends applying zero-trust, enforcing least-privilege and Just-in-Time access, and adopting ephemeral secrets and automated rotation. It highlights secrets and Privileged Access Management solutions such as KeeperPAM to centralize secrets, monitor privileged sessions, and make machine identities auditable and manageable at scale.

🔒 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.

🤖 OpenAI has begun rolling out a new variant of its coding agent, labeled GPT-5.2-Codex-Max, to a subset of paying users who have spotted the model identifier when querying Codex. The release follows December’s introduction of Codex with GPT-5.2 and appears to deliver enhanced long-task persistence, repository context compaction, improved tool reliability, better Windows workflow handling, and stronger vision for interpreting screenshots, UI bugs, and diagrams. Given the prior uplift seen with GPT-5.1-Codex-Max, this Max tier is expected to provide another notable performance bump, and OpenAI may publish formal details in the coming days.

🔐 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.

🔐Agentic AI introduces a new class of identity that behaves with humanlike intent yet scales and persists like machines. Traditional IAM and PAM were designed for employees and predictable workloads; AI agents are decentralized, easy to create, cross‑platform, and often granted broad privileges, creating serious blind spots. CISOs should apply lifecycle management: assign clear ownership tied to the identity provider, define explicit measurable purpose and scope, enforce least privilege, maintain continuous visibility to detect privilege drift, and automate revocation when agents go idle.

🔒 As AI hype settles, CISOs are refocusing 2026 priorities on resilience, rapid detection, and measurable outcomes. They favor engineering-driven architecture for cloud stability, AI-enabled orchestration to cut dwell time, and broad identity and privilege governance for human and non-human accounts. Visibility and SaaS discovery will curb shadow AI use, while security baked into agentic AI and post-quantum preparedness (cryptographic inventories and vendor roadmaps) become essential. Turning security into a visible trust signal and linking spend to ROI rounds out the agenda.

🔒 CrowdStrike and NVIDIA operationalized Nemotron LLMs to enable natural-language-to-CQL translation inside the Falcon platform. They leveraged millions of analyst queries, AST-based deduplication, and a PII scrubbing pipeline, then used NVIDIA NeMo Data Designer to generate synthetic natural-language descriptions for fine-tuning. Fine-tuning Llama-3.3-Nemotron-Super-49B-v1.5 with LoRA produced improved accuracy, interpretability through intermediate reasoning, and 96% valid-query accuracy versus frontier alternatives.

⚡ Google is testing a new image AI called Nano Banana 2 Flash, positioned as the fastest model in the Gemini Flash lineup. It aims to deliver quicker, more affordable image generation and editing than the existing Nano Banana Pro, though it will not match the Pro’s top-end capability for complex, high-accuracy creative tasks. The model was spotted on X by leaker MarsForTech and appears to prioritize speed and cost over fidelity.

🤖 Generative AI is rapidly transforming CyberOps by automating routine tasks, accelerating investigations and raising overall team productivity. Tools—some developed in-house and some by vendors—assist with forensics, incident response, log analysis, orchestration, vulnerability management and reporting. While AI scales capabilities and elevates junior staff, leaders stress the need for AI governance, prompt engineering skills and human oversight to manage risk.

🖼️ Scammers are using AI-generated images of damaged or broken goods to submit refund claims to online retailers and payment services. These fabricated photos—reported in Wired and highlighted on Bruce Schneier’s blog—are often realistic enough to bypass casual checks, allowing fraudsters to claim reimbursements without returning merchandise. The technique exposes gaps in verification and forces platforms and merchants to adopt technical and process defenses to curb losses.

🔒 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.

🛡️ 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.