All news with #prompt security tag

🧭 Google Cloud Consulting outlines the four-part SAVE method to produce trustworthy, brand-aligned generative media. The guidance emphasizes separating fact extraction from creative generation, locking down core claims, and enforcing deterministic checks before and after generation. It advocates human-in-the-loop review at the script/storyboard stage and programmatic safety gates to reduce fabrications and preserve brand integrity.

🔒 Agents need autonomy with enforceable limits in corporate settings. Without scoping, agentic flows expand access and may switch credentials (e.g., from read-only to admin) to complete tasks, creating audit and control gaps. Controls should inspect tool calls and context — operation, arguments, identity, target — and be placed where they can actually block disallowed actions. Multiple complementary enforcement points exist (managed settings, runtime hooks, gateways, sandboxes, endpoint controls, credential scoping, API management), each with trade-offs and coverage gaps.

🔒 This post explains how a steering file configures an AI model to perform structured, evidence-based vulnerability triage with the consistency of an experienced analyst. It outlines five configuration sections that enforce structural verification, evidence-based scoring, infrastructure-aware prioritization, and threat-intel boosts. The steering file encodes team methodology so the model applies it uniformly across analyses and reduces hallucinations and false positives.

🧭 This recap summarizes a conversation with Ryan Lopopolo on building autonomous coding agents using an agent harness around an LLM. It explains how harness engineering, shifting interventions left, and providing discoverable tools and documentation enable agents to operate autonomously and produce reviewable artifacts like pull requests. The piece also covers practical harness patterns, tooling choices, and advice to avoid bespoke scaffolding.

🛡️ The UK NCSC has issued interim advice urging organizations deploying autonomous AI agents to use sandboxing, human oversight and tightly controlled access to limit unintended or malicious activity. It recommends assessing required autonomy, threat-modeling prompts, tools and networks, and avoiding sole reliance on model-level safeguards. For higher-risk deployments the agency advises robust sandboxes, deny-by-default network controls, separate execution and inference infrastructure, and short-lived, minimal credentials. Organizations should assign distinct identities to agents, maintain named human oversight with real-time monitoring, log agent activity, and ensure the ability to halt autonomous operations immediately. The guidance is interim and will be superseded by formal guidance under development.

🔎 This article collects the top prompts Google Cloud developers and leaders use repeatedly to produce higher-quality work, from building specs and PRDs to thorough code reviews and permission checks. It explains each prompt, who contributed it, and why it helps—covering testing, cleanup, trade-off analysis, research-driven audits, and iterative improvement. The piece emphasizes practical guardrails and collaboration with AI to avoid overconfidence and brittle outputs.

📎 Modern phishing now prioritizes speed over persuasion. By forcing immediate downloads via trusted cloud providers (for example Dropbox?s dl=1), attackers remove the preview step and exploit double extensions and hidden OS behavior to disguise executables. Cortex Email Security applies deep static analysis, behavioral signals, and LLM-based intent classification to detect forced-download parameters, identity-bound cloaking, and rotating social-engineering lures before they reach endpoints.

🔐 Google outlines a continuous, layered approach to mitigating indirect prompt injection (IPI) across Workspace with Gemini, combining proactive discovery, synthetic data generation, and iterative defenses. Human and automated red-teaming, an AI Vulnerability Rewards Program, and OSINT monitoring are used to catalog and expand attack variants. Deterministic configuration controls, ML retraining, LLM prompt hardening, and model-level defenses are validated through comparative testing to reduce IPI success while preserving routine performance.

🔒 Enterprise AI guardrails meant to prevent misuse are increasingly blocking legitimate defensive activity, creating an asymmetry that favors attackers. Widely deployed, enterprise-approved models often refuse realistic phishing simulations, exploit proofs-of-concept, or multi-step red-team scenarios once prompts resemble real-world attacks. Attackers evade these limits using jailbroken models, open-source deployments, fine-tuning, and underground toolkits. The article calls for authorization-based access, purpose-built security sandboxes, and vetting workflows so safety controls protect against misuse without crippling defenders.

⚠️Microsoft warns that legitimate companies are embedding hidden instructions in 'Summarize with AI' buttons to bias chatbot memory and recommendations. The Defender Security Research Team calls this AI Recommendation Poisoning, finding more than 50 distinct prompts from 31 firms across 14 industries that attempt to make assistants 'remember' and favor a source in future conversations. The technique uses prefilled URL parameters and turnkey tools like CiteMET, and Microsoft advises users and organizations to audit assistant memory, avoid untrusted AI links, hover over AI buttons, and hunt for suspicious prompt keywords.

🧠 Microsoft warns that some websites and apps embed hidden prompts in 'Summarize with AI' features to influence enterprise assistants. These concealed instructions—termed AI recommendation poisoning—can persist in a user's AI memory and bias future responses across industries including finance, health, legal, and security. Researchers found 50 instances from 31 companies and note that open-source tools make the tactic easy to deploy. Users and administrators should audit saved assistant data and block suspicious links or URL patterns.

🔒 Most security leaders assume they know where sensitive data resides, but rapid AI adoption has created a new exposure surface in AI inference traffic. Prompts often contain source code, contracts, PII and proprietary workflows that flow through application layers, logs and third‑party services without classification or adequate controls. Traditional protections — transport encryption, legacy DLP and standard logging practices — frequently fail to prevent prompt leakage, producing an often invisible and growing enterprise risk.

🔒 OpenClaw is an open-source, agentic AI assistant that can run locally or on servers, connect to LLMs and external APIs, and autonomously perform actions such as sending email or controlling browsers. Its local storage of config and broad access (files, terminals, sometimes root) makes misconfigured deployments attractive as backdoors. CrowdStrike observed rapid adoption and internet-exposed instances, and recommends discovery, runtime guardrails, and automated removal integrated into detection workflows.

🔒 A recent analysis by koi.ai, highlighted by Bruce Schneier and Boing Boing, reports that the Urban VPN Proxy browser extension is surreptitiously intercepting conversations across multiple AI services. The extension embeds dedicated executor scripts for ten AI platforms and captures every prompt, every response, conversation identifiers, timestamps, session metadata, and the specific model or platform used. Harvesting is enabled by default via hardcoded flags and runs continuously in the background regardless of whether the VPN is active; there is no user-facing toggle and the only effective remediation is to uninstall the extension.

🔍 This spotlight report examines how AI is reshaping IT careers across roles—from developers and SOC analysts to helpdesk staff, I&O teams, enterprise architects, and CIOs. It identifies emerging functions and essential skills such as prompt engineering, model governance, and security-aware development. The report also offers practical steps to adapt learning paths, demonstrate capability, and align individual growth with organizational AI strategy.

🔒 Cloudflare introduces AI prompt protection inside its Data Loss Prevention (DLP) product on Cloudflare One, designed to detect and secure data entered into web-based GenAI tools like Google Gemini, ChatGPT, Claude, and Perplexity. The capability captures both prompts and AI responses, classifies content and intent, and enforces identity-aware guardrails to enable safe, productive AI use without blanket blocking. Encrypted logging with customer-provided keys provides auditable records while preserving confidentiality.