The newest AI resources are on the AI page. For a reading order instead of a date order, see the AI learning path.
| Date Added | Link | Excerpt |
|---|---|---|
| 2026-06-12 2026 | How I Built a Burp Extension Efficiently with Claude intermediate Burp | Claude AI significantly streamlined the development of a Burp extension that identifies non-standard HTTP headers. Previously, coding was the bottleneck, but Claude now handles much of that. The extension, inspired by Claude, helps security researchers by flagging uncommon headers that could indicate injection vulnerabilities. The development process involved using Claude to brainstorm ideas and generate a prototype, leveraging the IANA registry of common HTTP headers as a filter. This highlights Claude's potential to accelerate security tool development. → infosecwriteups.com |
| 2026-06-12 2026 | AI Security: explanation to Exploitation || Part 1 beginner | This article introduces AI security, focusing on "jailbreaking" large language models (LLMs) and AI-powered tools. With the widespread adoption of AI in organizations like Meta and Google for task automation, understanding and exploiting vulnerabilities in these systems is becoming crucial. The author plans to delve into the methods and extent of jailbreak exploitation. → infosecwriteups.com |
| 2026-06-12 2026 | How to Validate Policy-as-Code Without Breaking Builds (Even When AI Writes the Code) intermediate 4 min read | Library for validating Open Policy Agent (OPA) Rego policies, particularly those generated by AI tools like Claude Code and Cursor. It offers an AI-assisted authoring capability that translates natural language descriptions into Rego, and a playground for evidence-based validation against real application artifacts pulled from a System of Record. This allows security teams to test policies in a realistic environment before deployment, preventing accidental build breaks and ensuring effective governance. → jfrog.com |
| 2026-06-12 2026 | Trust No Skill: Integrity Verification for AI Agent Supply Chains advanced 8 min read Supply Chain | Library for auditing AI agent skills using Behavioral Integrity Verification (BIV). BIV compares a skill's declared capabilities against its actual execution across metadata, code, and natural-language instructions. It identifies deviations, classifying them as developer oversight or adversarial intent, and detects multi-stage attack chains like credential exfiltration and remote code execution (RCE). → unit42.paloaltonetworks.com |
| 2026-06-11 2026 | Agentic Browser Security: 2025 Year-End Review news 4 min read Mobile | Survey of Agentic Browser security, detailing 2025's advancements and risks. This review covers offensive research including Zero-Interaction Exfiltration, Scamlexity, Gemini Trifecta, CometJacking, Tainted Memories, HashJack, and Task Injection. Defensive strategies like Human-in-the-Loop (HITL), Reinforcement Learning, Architectural Isolation, and Secondary LLM Critics are examined, alongside Gartner's recommendation to block AI browsers and Wiz's advice on isolation, human oversight, and limiting blast radius. → wiz.io |
| 2026-06-11 2026 | AI-Powered Forensics, at Cloud Speed news 4 min read | Tool for AI-powered cloud forensics, Wiz provides a tiered approach to forensic visibility. It automatically captures targeted, full-context evidence from affected containers and hosts when suspicious activity is detected, then uses AI to analyze this data, surfacing clear conclusions to aid in rapid threat triage and investigation by SOC and DFIR teams. → wiz.io |
| 2026-06-11 2026 | AI Agents vs Humans: Who Wins at Web Hacking in 2026? news 10 min read Bug Bounty | Library evaluating AI agents Claude Sonnet 4.5, GPT-5, and Gemini 2.5 Pro on 10 lab challenges modeled after real-world web vulnerabilities. These models successfully solved 9 challenges, demonstrating proficiency in directed tasks and multi-step reasoning, such as bypassing authentication in VibeCodeApp and identifying frameworks from error messages in Bank Actuator. However, performance degraded in broad-scope scenarios, increasing cost by 2-2.5 times, and agents showed limitations in creative reconnaissance, failing to utilize professional tools or discover vulnerabilities like exposed GitHub secrets without explicit instruction. → wiz.io |
| 2026-06-11 2026 | Hacking Moltbook: The AI Social Network Any Human Can Control news 8 min read API Sec Secrets | Writeup on a Supabase misconfiguration in the AI social network Moltbook, which exposed 1.5 million API authentication tokens, 35,000 email addresses, and private messages. The vulnerability stemmed from a lack of Row Level Security (RLS) on the Supabase database, allowing unauthenticated read and write access to sensitive data, including account takeover credentials and plaintext API keys. This incident highlights security risks in "vibe-coded" applications, drawing parallels to previous issues like the DeepSeek data leak. → wiz.io |
| 2026-06-11 2026 | Building AI Security Together: New Ways to Partner with Wiz for AI Security in 2026 news 3 min read | Framework for AI Security integrates the Wiz Integration Network (WIN) with new AI capabilities. It introduces the WIN MCP, a standardized integration framework providing in-IDE access to Wiz APIs for faster, more secure development of plugins and custom workflows. Winnie, the WIN Community Wizard Agent, further accelerates builders by providing quick answers and documentation navigation within the WIN developer Slack community. These tools reduce friction for building secure, high-quality integrations, bolstering the new AI Security category within WIN, which includes AI Security Posture Management, runtime guardrails, agentic AI security, adversarial testing, and governance. → wiz.io |
| 2026-06-11 2026 | Introducing AI Cyber Model Arena: A Real-World Benchmark for AI Agents in Cybersecurity news 2 min read API Sec | Benchmark suite of 257 real-world challenges assessing AI agents across zero-day discovery, CVE detection, API security, web security, and cloud security (AWS, Azure, GCP, Kubernetes). The AI Cyber Model Arena evaluates AI models' offensive capabilities, separating agent and model effects with a multi-agent x multi-model matrix. Scoring uses deterministic rubrics and pass@3, with challenges run in isolated Docker containers to ensure fairness and realism. Results highlight that offensive capability is jointly determined by model and agent scaffold, with performance varying significantly across domains. → wiz.io |
| 2026-06-11 2026 | Would You Click ‘Accept’? Automatically detecting malicious Azure OAuth applications using LLMs intermediate 11 min read AuthN | Tool for detecting malicious Azure OAuth applications, leveraging insights from homoglyph attacks and analysis of real-world campaigns. This pipeline identifies suspicious applications by comparing their characteristics against a baseline of legitimate integrations, flagging deviations in publisher verification, homepage URLs, and application owner domains. The research highlights common gaps in Azure service principal management and the risk of consent fatigue, leading to potential privilege escalation and persistent access for attackers. → wiz.io |
| 2026-06-11 2026 | What an 'Aha' Moment with an Org Admin Token Taught One DevSecCon Speaker About AI Security beginner 3 min read Supply Chain | Talk slides from DevSecCon 2025 detail securing AI-native development pipelines, focusing on the risks introduced by agentic AI and MCP servers. The session highlights the vulnerability of using Org Admin Tokens with GitHub MCP servers, which could lead to extensive data compromise, and proposes a security checklist for these AI tools. Attendees will learn that AI integration into pipelines demands security scrutiny comparable to traditional code and tools, benefiting Platform Engineers, DevOps Engineers, Developers, and Security Professionals. → snyk.io |
| 2026-06-11 2026 | Secure Your AI Workflows: New Governance & Visibility Features from Snyk beginner 4 min read | Platform features enhance AI workflow security by providing governance and visibility. The Ignore Approval Workflow allows developers to request findings bypass while AppSec teams maintain oversight. CLI Upload persists local scan results for comprehensive risk tracking. Group by Dependency View aids vulnerability prioritization by consolidating issues per dependency. The Export API integrates Snyk data into internal systems for reporting, and Agent Usage to IDE reports track AI coding assistant utilization across environments. New Snyk Learn Reports measure developer education program effectiveness. → snyk.io |
| 2026-06-11 2026 | Beyond the Hype: 5 Major Reasons to Attend DevSecCon 2025 news 4 min read | Workshop slides from DevSecCon 2025 covering AI's impact on software development, including securing AI-generated code, empowering developers with guardrails, proving program success through metrics, and extending security to AI-powered applications like LLMs and agents. The event aims to equip leaders with strategies for scaling businesses securely in the age of AI. → snyk.io |
| 2026-06-11 2026 | Snyk and Cognition partner to enhance security for AI-native development news 2 min read | Library integrations, Snyk for Devin and Snyk for Windsurf, embed Snyk Studio's security intelligence into Cognition's AI-native developer tools. These integrations offer real-time security scanning within development workflows, enabling accelerated agent-led remediation by AI agents like Devin and Windsurf. This partnership aims to scale security processes alongside AI-driven development velocity, reducing security backlogs and improving resource allocation by automating routine fixes. → snyk.io |
| 2026-06-11 2026 | Why We Built Evo — From My Heart news 3 min read | Library: Evo by Snyk is the world's first Agentic Security Orchestrator, designed to address the evolving security challenges posed by AI-native systems. It proactively defends against threats like model supply chain attacks, memory abuse, prompt manipulation, and agent escalation. Evo observes AI attack surfaces, orients risk with contextual intelligence, and automatically enacts response plans using specialized AI security agents. This approach aims to integrate security seamlessly into AI development, enabling innovation without compromising safety. → snyk.io |
| 2026-06-11 2026 | DevSecCon 2025 Recap: Securing the AI Revolution Together news 7 min read | Library for securing AI-accelerated DevSecOps, focusing on shifting security left to the developer's first prompt. It introduces capabilities for improved IDE and pull request experiences, enhanced asset visibility and risk-based prioritization for AppSec teams, and a new dependency view in Snyk Open Source. The library also details Evo by Snyk, an agentic security orchestration system for AI-native applications, and highlights a partnership with Cognition to embed security intelligence directly into AI workflows. → snyk.io |
| 2026-06-11 2026 | Snyk Studio: Now for All Customers, Powering Secure AI Development at Scale news 8 min read | Library for securing AI-driven development, Snyk Studio now empowers all customers with enterprise-wide rollout capabilities via Jamf and a VS Code extension. It embeds security intelligence directly into AI coding assistants like Cursor and Copilot, enabling "Secure at Inception" through automatic code scanning and remediation directives for new code. This approach aims to prevent vulnerabilities from being introduced and addresses existing security debt through conversational remediation, significantly accelerating backlog clearance. → snyk.io |
| 2026-06-11 2026 | The Agentic OODA Loop: How AI and Humans Learn to Defend Together beginner 7 min read | Library Evo functions as an agentic security orchestrator, enabling human and AI collaboration to defend agentic systems at machine speed. It embodies the OODA loop by perceiving AI architecture, reasoning about risks, orchestrating security workflows, and learning continuously from human engineers and system behavior. Evo amplifies AI Security Engineers, reducing time spent on tactical tasks like scanning models and correlating alerts, allowing them to focus on strategic work and apply lessons automatically across systems. → snyk.io |
| 2026-06-11 2026 | Secure by Design: The Future of Threat Modeling for AI-Native Applications intermediate 6 min read | Library for continuous, automated threat modeling of AI-native applications, including LLMs and autonomous agents. Evo by Snyk directly connects to code, cloud infrastructure, and runtime data to generate dynamic system models that update with software evolution, eliminating drift. It provides contextual, actionable mitigations for vulnerabilities such as prompt injection, data exfiltration via AI models, and data poisoning, integrating into CI/CD pipelines for a secure-by-design workflow. → snyk.io |
| 2026-06-11 2026 | Our AI Agent Now Has a Security Conscience: Introducing the JFrog Plugin for Claude Code intermediate 7 min read | Plugin for Claude Code that integrates JFrog's Software Supply Chain Platform, providing AI coding agents with real-time security scanning, package safety checks, and governed MCP server management. It enables artifact traceability via JFrog Artifactory, dependency governance through JFrog Curation, and controlled MCP server usage via Agent Guard, ensuring AI-generated code adheres to organizational security and compliance policies by shifting governance earlier in the development workflow. → jfrog.com |
| 2026-06-11 2026 | The Governance Gap: What IDC’s 2026 Data Reveals About AI and the Software Supply Chain news 3 min read Supply Chain | Survey of AI's impact on the software supply chain, revealing that corporate AI adoption is outpacing governance readiness. The IDC 2026 report highlights "Shadow AI" as a significant unmanaged threat, with developers using unvetted tools and AI-generated code. The entry stresses that AI agents cannot self-police compliance and may introduce vulnerable dependencies from repositories like npm or PyPI. The solution involves building platform-level guardrails within the software supply chain to manage AI adoption safely. → jfrog.com |
| 2026-06-10 2026 | AI Agents May Always Fall for Prompt Injections advanced 1 min read | Framework analyzing prompt injection vulnerabilities in AI agents through the lens of Contextual Integrity (CI). It demonstrates how current defenses fail against contextual manipulation and proposes an impossibility result: adversaries can always craft contexts that legitimize blocked flows, suggesting current research addresses a diminishing attack surface. The framework offers a principled approach for evaluating context-sensitive failures and designing CI-aware alignment for autonomous agents. → arxiv.org |
| 2026-06-10 2026 | Building an Agentic Cloud Security Ecosystem: A Reference Architecture with Wiz MCP and Infosys Cyber Next intermediate 7 min read | Reference architecture detailing an agentic cloud security ecosystem, leveraging Wiz MCP and Infosys Cyber Next. This model uses intelligent agents for detection, investigation, and remediation, powered by the Wiz Security Graph's contextual data. It highlights the Wiz Remote MCP Server as a key enabler for AI-driven workflows and illustrates an intelligent S3 remediation scenario involving discovery, investigation, and human-approved remediation agent actions. → wiz.io |
| 2026-06-10 2026 | Security Insights Where Work Happens: Notion Custom Agents + Wiz MCP intermediate 3 min read AuthZ | Integration that connects Wiz cloud security insights with Notion Custom Agents, enabling AI teammates to answer security questions, generate reports, and investigate risks directly within Notion workspaces. This allows teams to access cloud security context where they collaborate, using features like the Wiz Cloud Questioner to query their environment and the Wiz Vulnerability Summarizer to automate security reporting, bringing actionable insights into everyday workflows. → wiz.io |
| 2026-06-10 2026 | Seeing AI Clearly: Building Visibility Across Modern AI Applications beginner 6 min read | Library for building visibility across modern AI applications, offering an implementation-agnostic approach to discover and inventory AI systems. It combines code analysis, agentless cloud detection, AI workload explanation, model invocation logs, and runtime signals to provide a unified view of AI components, including models, agents, tools, guardrails, identities, and AI tool adoption. This comprehensive visibility is foundational for understanding AI construction, ownership, and enabling subsequent security measures like posture risk assessment and threat detection. → wiz.io |
| 2026-06-10 2026 | Understanding and Reducing AI Risk in Modern Applications beginner 8 min read | Library for identifying and mitigating risks in AI applications. It analyzes AI systems across infrastructure, models, data, and application layers, detecting vulnerabilities stemming from component interactions. The library helps pinpoint risks like prompt injection, insecure tool usage, embedded credentials, and misconfigured AI platforms, offering comprehensive visibility to prevent insecure AI systems from reaching production and ensure correct protections are in place. → wiz.io |
| 2026-06-10 2026 | AI Runtime Threat Detection: From Input to Real-World Impact intermediate 4 min read | Library for AI runtime threat detection that monitors behavior across the model, workload, and cloud layers, correlating activity from input to real-world impact. It moves beyond basic prompt filtering to detect when AI agents take risky or malicious actions, even with benign-looking prompts. By applying AI context, it transforms raw signals into actionable understanding, linking runtime events to their originating code or configuration for faster root cause analysis and remediation. The library's approach provides visibility into complex attack chains, such as those involving prompt injection leading to reverse shells and credential exfiltration. → wiz.io |
| 2026-06-10 2026 | Introducing Wiz Agents & Workflows: Security at the Speed of AI beginner 7 min read AuthZ | Library introducing Wiz Agents and Workflows, AI-powered security systems that reason, investigate, and take action across code, cloud, and runtime. The Red Agent functions as an AI attacker identifying logic-driven vulnerabilities, the Blue Agent acts as a threat investigator by gathering evidence, and the Green Agent drives remediation by pinpointing root causes and providing actionable fixes. Integrated into Workflows, these agents orchestrate automated responses and human-approved actions, streamlining security operations from discovery to resolution. → wiz.io |
| 2026-06-10 2026 | Introducing Wiz AI Application Protection Platform (AI-APP) beginner 6 min read | Platform that secures AI applications end-to-end, connecting infrastructure, data, access, models, agents, and applications from code to runtime. It builds a complete AI inventory, maps cross-layer risk correlated with frameworks like OWASP Top 10 for LLM Applications, and provides runtime threat detection across model activity, workload execution, and the cloud layer. Integrations with Cloudflare, TrojAI, and Pillar Security enrich findings with cloud context, enabling teams to prioritize exploitable risks and drive remediation through agents that identify risk, determine fixes, and investigate threats. → wiz.io |
| 2026-06-10 2026 | Introducing the Wiz Red Agent- AI-Powered Attacker intermediate 8 min read | Library for AI-powered attack surface management, the Wiz Red Agent, autonomously discovers and validates complex exploitable risks across cloud environments and proprietary APIs. It leverages deep cloud context, world-class attacker expertise, and adaptive, reasoning-based exploitation to uncover vulnerabilities missed by traditional scanning and manual research, including authorization flaws and business logic errors. The Red Agent integrates with the Wiz platform to correlate application-layer risks with cloud infrastructure, enabling better prioritization and remediation guidance. → wiz.io |
| 2026-06-10 2026 | AI Threat Readiness Pillar 2: Accelerate Patching and Response intermediate 7 min read | Library for accelerating patching and response in AI threat readiness, this resource details how to establish clear ownership, identify root causes across cloud configuration to source code, determine optimal fix paths with environment-specific context, and automate remediation workflows. It highlights Wiz's Green Agent for tracing vulnerabilities to their source and recommending the most efficient fix, alongside Wiz Workflows for orchestrating the entire remediation chain and shifting fixes left to prevent recurrence. → wiz.io |
| 2026-06-10 2026 | Snyk and Continue Partner to Embed AI-Powered Security into Every Step of the Developer Workflow news 3 min read | Library integrating Snyk and Continue automates security scans for code, dependencies, IaC, and containers using natural language commands within the developer workflow. This partnership enables faster vulnerability remediation through AI-generated, validated code fixes and proactive policy enforcement, allowing developers to address security without context switching. The integration supports Snyk's SAST, SCA, and IaC security tools directly in IDEs and CLIs, aiming to make "secure by default" a reality. → snyk.io |
| 2026-06-10 2026 | Beyond Automation: Securing Low-Code Agentic AI with MCP Guardrails beginner 3 min read | Library for securing low-code agentic AI, MCP Guardrails standardizes AI agent interaction with external tools via the Model Context Protocol (MCP). It incorporates a scanner layer for validating code, data, and commands, and an observability layer for comprehensive logging and traceability. This approach, supported by Toxic Flow Analysis (TFA), integrates static configuration data with dynamic runtime information to proactively detect vulnerabilities and mitigate risks like indirect prompt injection in autonomous AI systems. → snyk.io |
| 2026-06-10 2026 | Why Threat Modeling Is Now Even More Critical for AI-Native Applications beginner 4 min read | Reference of AI-native threat modeling practices, emphasizing the shift from manual, static workshops to continuous, adaptive processes. It details new attack surfaces like data poisoning and adversarial attacks, the unpredictable behavior of AI models, and the challenges of rapid deployment cycles, regulations like the EU AI Act, and complex ecosystems. The article advocates for automated asset discovery, dynamic risk modeling, and integrated remediation to maintain security posture at the speed of AI development. → snyk.io |
| 2026-06-10 2026 | How Snyk Studio for Qodo Is Closing the AI Security Gap news 3 min read | Library integrating Snyk's security intelligence with Qodo's Agentic Code Quality Platform. Snyk Studio for Qodo embeds security directly into the AI development workflow, leveraging Snyk's SAST and SCA engines. This allows developers to identify and fix vulnerabilities as they code within their IDE. The solution also addresses existing security debt through natural language prompts and automated remediation, aiming to resolve issues in minutes and accelerate secure AI-driven development at scale. → snyk.io |
| 2026-06-10 2026 | Scaling AI Security: How Evo Complements New Agentic Tools beginner 7 min read | Library for scaling AI security, Evo by Snyk, complements agentic tools like OpenAI's Aardvark by offering stable, reproducible findings and integrating security earlier in the development lifecycle. It provides multi-layer AI threat detection, mature dynamic testing (DAST) and software composition analysis (SCA) engines, and native governance features to support enterprise workflows and compliance without unpredictable token-based costs. → snyk.io |
| 2026-06-10 2026 | Snyk Log Sniffer: AI-Powered Audit Log Insights for Security Leaders beginner 4 min read | Tool for AI-powered analysis of Snyk audit logs, transforming raw data into actionable intelligence for security and engineering leaders. Log Sniffer leverages Google Gemini AI to provide executive summaries, answer security questions in natural language, and monitor audit events in real-time. It seamlessly integrates with the Snyk API, offering intelligent filtering and transforming complex security events into understandable insights, improving decision-making and risk mitigation. → snyk.io |
| 2026-06-10 2026 | When Speed Meets Security: Snyk Studio for Kiro news 4 min read | Library integration embedding Snyk Studio into Amazon Kiro’s agentic IDE, allowing developers to prevent new security risks at inception. This integration runs `snyk_code_scan` for generated code, attempts fixes with context from Snyk scans, and rescans to ensure resolution. It also addresses existing vulnerabilities through natural language prompts, identifying issues across code, dependencies, and IaC, then validating AI-generated fixes. → snyk.io |
| 2026-06-10 2026 | Run AutoMCP To Supercharge Your AI Agent with Libraries MCP Servers intermediate 3 min read | Tool for automating Model Context Protocol (MCP) server setup in AI-driven development environments. AutoMCP, an npm command-line tool, detects coding tools and project dependencies to configure MCP servers, enabling AI agents to autonomously run Snyk scans for early vulnerability detection. This integration, facilitated by Snyk Studio, embeds security directly into AI-assisted workflows, ensuring both human-written and AI-generated code is secure. → snyk.io |
| 2026-06-10 2026 | How Snyk Helps Federal Agencies Prepare for the Genesis Mission Era of AI-Driven Science beginner 3 min read Supply Chain | Library for securing AI-driven scientific missions, Snyk provides federal agencies with visibility into open source libraries, containers, and IaC templates within their software supply chains. It integrates security into CI/CD, model-training, and data pipelines, catching vulnerabilities and misconfigurations before deployment. The platform also addresses cloud and container security for AI compute systems, detecting misconfigurations and securing container images. By embedding security directly into developer workflows with automated fix recommendations and IDE plug-ins, Snyk operationalizes "secure by design" principles to accelerate discovery without compromising trust, aligning with federal expectations like Secure by Design, NIST 800-218, and EO 14028. → snyk.io |
| 2026-06-10 2026 | Old AI Security vs Evo: Watch Agentic Security Replace Weeks of Manual Work beginner 4 min read | Library for agentic AI security orchestration, Evo by Snyk, addresses emergent threats like prompt injection, data poisoning, and supply chain risks inherent in AI-native applications. It automates security workflows, including AI Bill of Materials (AI-BOM) generation, MCP Scan CLI for identifying risky components, and continuous AI red teaming to keep pace with evolving AI systems, contrasting with traditional, manual application security methods. → snyk.io |
| 2026-06-10 2026 | Evo Adds CycloneDX Support to Give Full AI Visibility news 4 min read Supply Chain | Library extending CycloneDX support to provide AI supply chain visibility. Evo's Discovery Agent now integrates with CycloneDX 1.6 AI ModelCards, enabling standardized AI-BOMs that detail model provenance, licensing, architecture (transformer, CNN), learning approach (supervised, self-supervised), and implementation paths. This addresses visibility gaps by offering a centralized inventory, tracking model origins from sources like HuggingFace, and providing granular insights into model type and task domain, making AI governance actionable. → snyk.io |
| 2026-06-10 2026 | Secure by Default: Why Snyk and Augment Code are the New Standard for AI Development news 2 min read | Partnership between Snyk and Augment Code that embeds Snyk's security intelligence into Augment Code's AI development platform. This integration provides real-time security scanning as developers write code, accelerated agent-led remediation for identified vulnerabilities, and governance at scale through custom Snyk rules applied to AI-generated code. The solution aims to make "Secure by Default" a reality for AI-driven development, reducing mean time to remediate and eliminating security as a manual bottleneck. → snyk.io |
| 2026-06-10 2026 | ServiceNow's Virtual Agent Vulnerability Shows Why AI Security Needs Traditional AppSec Foundations news 6 min read AuthN AuthZ | Library for securing agentic AI applications, emphasizing foundational application security alongside AI-specific controls. It highlights the ServiceNow Virtual Agent vulnerability, stemming from broken API authentication and excessive agent privileges, not novel AI issues. The library recommends a layered approach including agent-aware threat modeling to identify risks before deployment, DAST with LLM-enhanced authorization testing to detect classic vulnerabilities, and AI red teaming to reveal catastrophic impact paths enabled by autonomous agents. It stresses principles like least privilege and strong API identity verification for comprehensive AI security. → snyk.io |
| 2026-06-10 2026 | Live From Davos: The End of Human-Speed Security news 4 min read | Report detailing "The End of Human-Speed Security: Defense in the Age of AI Agents" highlights the rapid shift to AI operating as quasi-autonomous agents, with 50% of security leaders reporting this reality. It discusses the weaponization of AI, citing state-backed attacks on Anthropic, and the resulting "visibility crisis" where AI adoption often occurs outside monitored systems. The report calls for industry standards and a move beyond manual security processes to address challenges posed by autonomous attacks and achieve machine-speed defense. → snyk.io |
| 2026-06-10 2026 | Introducing the AI Security Fabric: Empowering Software Builders in the Era of AI news 8 min read | Library for securing applications in the age of AI, the Snyk AI Security Platform operationalizes a prescriptive path. It addresses AI-accelerated DevSecOps by fortifying traditional software supply chains, secures AI-driven development by embedding security into coding assistants like Snyk Studio, and defends AI-native applications with the agentic security orchestrator Evo by Snyk. This unified approach weaves security directly into every stage of modern software creation, adapting to dynamic systems and operating at machine speed to build trust and mitigate risks introduced by AI. → snyk.io |
| 2026-06-10 2026 | The Prescriptive Path to Operationalizing AI Security news 14 min read | Framework for operationalizing AI security, the Prescriptive Path provides an opinionated operating model with three phases: Stabilize, Optimize, and Scale. It focuses on building trust, reducing real risk, and sustaining governance by emphasizing outcomes over individual tools or checklists. The path guides organizations on how to apply security capabilities deliberately, from achieving foundational visibility and implementing guardrails for AI-generated code, to accelerating remediation and enabling autonomous defense in AI-native systems. → snyk.io |
| 2026-06-10 2026 | Zero-Click IP Leak in a Privacy Search Engine: Indirect Prompt Injection & Silent Patching intermediate | A security researcher discovered a zero-click IP leak vulnerability in Kagi Search, a privacy-focused search engine. The vulnerability exploited an indirect prompt injection technique using a Markdown trick to deanonymize users. This allowed the attacker to force a victim's browser to reveal their IP address, undermining Kagi's core privacy promise. Kagi Search has since quietly patched the vulnerability, indicating a "Not Applicable" status for the report, which the researcher interprets as a silent fix. → infosecwriteups.com |
| 2026-06-10 2026 | Mythos Doesn't Deploy Itself news 5 min read | Toolset analysis highlighting how AI models like ChatGPT, Claude, and Gemini are impacting vulnerability research. It discusses how skilled researchers leverage LLMs with effective harnesses, referencing Niels Provos's use of IronCurtain to find zero-days, while less skilled practitioners produce inaccurate, polished reports, leading to issues like those seen with Bugcrowd and HackerOne's bug bounty programs. The core argument posits that human judgment and expertise in orchestration and validation remain critical, regardless of model capabilities, as demonstrated by findings in Cisco's Talos and Anthropic's red team efforts. → bishopfox.com |