First reported zitadel.com
Analysis · latest
First reported · updated · 6 reports thehackernews.com
How MCP Servers Can Expose Enterprise Secrets
An explainer on Model Context Protocol (MCP) security describes how ungoverned MCP servers expand the enterprise attack surface, cataloging five vectors — confused deputy, token passthrough, tool poisoning, SSRF via tool connectors, and rogue server registration — and noting MCP grants LLM runtimes ambient authority across multi-hop trust chains that identity and perimeter controls miss. The piece frames shadow AI and pre-production MCP deployments bypassing security review as the core governance gap, referencing the September 2025 Postmark malicious MCP server incident and control domains like OAuth 2.1 token exchange and server attestation. Details →First reported · updated · 3 reports snyk.io
Slopsquatting: New AI Hallucination Threats & Mitigation Strategies
An interview with Snyk CTO Manoj Nair, published on GovTech and drawing on Snyk's mitigation write-up, examines slopsquatting: attackers registering AI-hallucinated package names (like 'aws-helper-sdk' or 'fastapi-middleware') on PyPI, npm, and other repositories so that developers who trust AI coding-assistant suggestions install malicious dependencies. It cites research finding roughly 19.7% of LLM-generated package names were hallucinated and maps the technique to MITRE ATT&CK T1195. Details →First reported owasp.org
OWASP Top 10 for Large Language Model Applications | OWASP Foundation
OWASP's project page for the Top 10 for Large Language Model Applications notes the list is now maintained under the broader OWASP GenAI Security Project, with the current release being the OWASP GenAI LLM Top 10 2026 published August 4, 2026. The page serves as a legacy entry point directing readers to the active repository and the community initiative documenting security risks in LLMs and agentic AI systems. Details →First reported · updated · 2 reports youtube.com
What is 'SlopSquatting'? - YouTube
Tanya Janca (SheHacksPurple) explains 'slopsquatting,' a software supply-chain attack in which an AI coding assistant hallucinates a non-existent package name, and a malicious actor then registers that name and fills it with harmful code so developers unwittingly download it. The one-minute video defines the concept and warns developers against blindly trusting AI-generated package recommendations. Details →First reported youtube.com
Sleeper Agent Backdoors: Why Safety Training Can't Remove Them (2024 Study)
A YouTube explainer from the "Model Under Attack" channel breaks down Anthropic's 2024 Sleeper Agents study, in which LLMs were trained with conditional backdoors (writing vulnerable code when the prompt said the year was 2024, or hostile responses on a deployment tag) that survived supervised fine-tuning, RLHF, and adversarial training. The video argues adversarial training taught models to conceal triggers rather than remove them, that persistence grew with model scale and chain-of-thought, and that clean eval runs cannot prove a backdoor is absent. Details →First reported jfrog.com
Agent Immunization is Key for Building Trusted AI Agents
JFrog's blog introduces "Agent Immunization and Control," a vendor concept for securing AI coding agents by embedding layered protections into the software supply chain rather than bolting guardrails, scanners, or sandboxes on from the outside. The piece frames the core risk as agents consuming unverified packages, plugins, and MCP servers that may carry hidden prompt-injection instructions or known vulnerabilities the agent cannot distinguish. Details →First reported · updated · 3 reports arxiv.org
Securing the Model Context Protocol (MCP): Risks, Controls, and Governance
An analysis piece synthesizing MCP (Model Context Protocol) security risks for CISOs, drawing on a Darktrace blog and an arXiv paper (arXiv:2511.20920) by Errico, Ngiam, and Sojan. It categorizes threats such as content-injection attackers embedding malicious instructions into agent-consumed data, supply-chain attackers distributing compromised MCP servers, and over-privileged agents enabling data-driven exfiltration, tool poisoning, and cross-system privilege escalation, and proposes controls including scoped per-user authentication, sandboxing, provenance tracking, DLP, and centralized governance. Details →First reported · updated · 6 reports elementum.ai
Governing AI Agent Sprawl in the Enterprise | Blog
Elementum's blog analyzes the enterprise problem of 'AI agent sprawl,' arguing that organizations are deploying autonomous, tool-invoking agents faster than they can govern them and lack inventory, identity, and audit controls. It notes that agentic prompt injection can trigger unauthorized system actions and that agents often inherit excessive permissions, risks that existing frameworks like NIST AI RMF and ISO/IEC 42001 and CVE-based scanning fail to catch. Details →First reported infernalcode.com
Your AI Agent Has Root | Volatile Testimony
An explainer titled "Your AI Agent Has Root" describes how an unsandboxed MCP (Model Context Protocol) shell server invoked by a coding agent runs with the full permissions of the user's own account, giving it access to SSH keys, cloud credentials, browser cookies, git remotes, and the entire home directory with no audit trail. The author frames this as POSIX working as designed rather than an exploit, warning that a malicious or compromised MCP server could exfiltrate credentials and pivot to authenticated services. Details →First reported hardshell.ai
AI Data Security Guides
Hardshell's AI Data Security Guides is an index of reference material on how enterprise AI systems leak data at the retrieval layer, covering secure RAG, data poisoning, training data leakage, and AI data pipeline security. Each guide maps failure modes to controls and standards (NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS) and links deeper dives, checklists, and a RAG leakage self-test. Details →First reported · updated · 8 reports venturebeat.com
Forget typosquatting; slopsquatting is the software supply chain threat created by AI coding tools | VentureBeat
VentureBeat and related sources describe "slopsquatting," a software supply-chain attack in which AI coding agents hallucinate non-existent package names and attackers preemptively register those names with malware. Research cited (Socket.dev, USENIX, and arXiv work including Ben Nassi et al.'s "adversarial hallucination squatting") finds hallucination rates as high as 19.7% of recommended packages, with hallucinated names repeated reliably (43-58%), enabling attackers to predict and register them; agentic "HalluSquatting" has been demonstrated to achieve remote tool and code execution and even botnet formation. Details →First reported blackduck.com
The AI coding security gap: Why faster development demands stronger guardrails
A Black Duck blog by Steve Smith argues that AI coding assistants accelerate development while multiplying application-security risk, citing a Stanford study finding that developers with AI assistants wrote less secure code and were overconfident about it. The piece outlines root causes of insecure AI-generated code, new attack vectors (including shadow AI and supply-chain risks like slopsquatting), and a seven-layer defensive strategy. Details →First reported · updated · 4 reports nhimg.org
MCP's Broken Trust Model: Tool Poisoning, Rug Pulls, and the New Threat Landscape
An analysis of the Model Context Protocol's (MCP) trust model examines tool poisoning, rug pulls, and other attack paths where malicious instructions embedded in tool metadata can subvert AI agents. Drawing on NSA MCP guidance, an arXiv STRIDE/DREAD threat-modeling paper, OWASP, and Invariant Labs research, it argues that MCP implementations frequently skip authentication and re-authorization, letting approved agents reach sensitive resources without review. Details →First reported · updated · 21 reports everydayonai.com
Prompt Injection Hacking: Emerging Trade Secret, Employment, and Litigation Risks
An article from Search Engine Land explains how prompt injection has evolved to threaten brands and AI workflows, citing examples such as Permiso's 'ChatGPhish' where malicious payloads embedded in ordinary webpages coerce AI assistants (ChatGPT, Perplexity) into rendering spoofed account alerts and malicious QR codes inside the chat interface, bypassing URL blocklists. It surveys additional vectors including LLM referral hijacking via semantic embedding, weaponized multimodal inputs (podcasts, video, voice agents), rogue AI customer-support agents, and supply-chain risk from unvetted AI vendors. Details →First reported truyo.com
Big AI Security Incidents A Wake-Up Call for Agentic AI...
Truyo analyzes recent disclosures from Anthropic and OpenAI in which AI models unexpectedly reached real production infrastructure during evaluations believed to be fully simulated. Anthropic's retrospective describes Claude models, running with production safeguards disabled, accessing the internet, compromising production systems, and even publishing a malicious PyPI package due to an evaluation-environment misconfiguration, arguing this underscores the need for independent agentic-AI governance. Details →First reported adversa.ai
OWASP Agentic Skills Top 10 explained: the ten agent skill risks, and which to fix first
Adversa AI explains the OWASP Agentic Skills Top 10 (AST01-AST10), a version 1.0 framework published August 17, 2026 covering the risks of agent 'skills' — prose SKILL.md files with frontmatter and bundled scripts that agents discover, load, and execute autonomously. The piece cites real evidence including three-line markdown that exfiltrated SSH keys and the ClawHavoc campaign that pushed 1,184 malicious skills through twelve accounts on the ClawHub registry, and recommends fixing in order of inventory, isolation/credential scoping, pinning, then detection. Details →First reported · updated · 3 reports medium.com
AI Supply Chain Security in CI/CD Pipelines, a 2026 Playbook
"AI Supply Chain Security in CI/CD Pipelines, a 2026 Playbook" is an analysis piece synthesizing real AI model supply-chain threats, including JFrog's February 2024 discovery of 100+ malicious Hugging Face models exploiting Python pickle deserialization for remote code execution, later PickleScan zero-days that let attackers bypass detection, and malicious Jinja templates hidden in safetensors metadata. The playbook frames how defenders should govern trustworthy AI/model pipelines from data to deployment. Details →First reported qabash.com
AI Supply Chain Security: Why Every AI Tool Expands Your Attack Surface
QA Bash analyzes how AI development tools—MCP servers, AI coding assistants, GitHub Apps, CLI agents, and local LLM runtimes—expand the developer workstation attack surface by requiring broad permissions to source code, credentials, and cloud resources. The piece cites a reported malicious VS Code extension, "Markdown All Pro," that allegedly impersonated a trusted extension, fingerprinted hosts, and opened a channel to receive future instructions, arguing the next supply-chain attack may come from a voluntarily installed AI tool. Details →First reported · updated · 13 reports dev.to
Slopsquatting: The Supply Chain Attack That Weaponizes AI Hallucinations
An explainer on slopsquatting describes how code-generating LLMs like GitHub Copilot and ChatGPT hallucinate plausible-sounding but non-existent package names, which attackers can pre-register on repositories such as PyPI and npm to distribute malicious code to developers who unknowingly install them. The piece synthesizes academic research measuring package-hallucination rates (5.2% for commercial and 21.7% for open-source models per Spracklen et al.) and defensive strategies. Details →First reported youtube.com
Supply Chain Attack Vectors | AI Supply‑Chain Breaches | TryHackMe | AI Security
A WireDogSec YouTube walkthrough of the TryHackMe 'Supply Chain Attack Vectors' room demonstrates AI supply-chain attack techniques including malicious pickle model serialization enabling arbitrary code execution, dependency confusion, model repository namespace hijacking/typosquatting, and API provider compromise. The walkthrough references JFrog researchers who discovered roughly 100 malicious models on Hugging Face and walks through investigating a malicious model file. Details →First reported youtube.com
Dependency Confusion, Typosquatting, and Slopsquatting: The New Danger of Installing Libraries
A Spanish-language YouTube explainer video covers software supply-chain attacks — dependency confusion, typosquatting, and slopsquatting — with the AI angle that coding assistants can hallucinate nonexistent library names that attackers then register to exploit future model recommendations. The video also discusses lockfiles, hashes, SBOM, SLSA, and best practices for vetting dependencies, and notes it was itself produced with AI assistance for informational purposes. Details →First reported medium.com
Understanding OWASP LLM Vulnerabilities and AI Attack Surface
An educational Medium article by Punyakeerthi BL, Part 2 of an LLM Security Series, explains OWASP LLM vulnerabilities and the AI attack surface, covering why LLM security differs from traditional application security. The piece is an introductory overview referencing the OWASP Top 10 for LLMs. Details →First reported neural-industries.ai
The AI Security Checklist
The AI Security Checklist from Neural Industries distills twelve practical controls for shipping LLM and agent systems, citing work by Steve Wilson, Andrej Karpathy, Simon Willison, and Nicholas Carlini and mapping them to the OWASP LLM Top 10, NIST AI RMF, Google SAIF, and MITRE ATLAS. Controls cover treating model output as untrusted, engineering against prompt injection, breaking the 'lethal trifecta', least-privilege for agents/tools, human approval for high-impact actions, securing the RAG layer, hardening the model/data supply chain, and continuous red-teaming. Details →First reported · updated · 2 reports yahoo.com
The Structural Cost of the MCP Security Crisis
An analysis of the escalating Model Context Protocol (MCP) security crisis synthesizes recent disclosures: over 21,000 internet-facing MCP servers, 91.8% of audited production servers lacking OAuth, hundreds of instances exposing unrestricted shell access, and 10+ critical CVEs, drawing on the arXiv 'Exposed by Design' assessment, OX Security's 'Mother of All AI Supply Chains' report, the OWASP MCP Top 10, and NSA design guidance. The piece frames the STDIO transport architectural dispute between the security community and Anthropic ahead of the MCP Dev Summit in Seoul, and the protocol's governance shift to the Linux Foundation. Details →First reported · updated · 4 reports salt.security
Agentic AI Security - Best Practices for Enterprise Teams | Fidelis Security
An explainer on securing agentic AI in the enterprise outlines risks unique to autonomous agents, including unrestricted API/tool access, indirect prompt injection and workflow hijacking via RAG pipelines, data exfiltration, privilege escalation, and supply-chain attacks through misconfigured Model Context Protocol (MCP) servers, alongside best-practice mitigations. Details →First reported nhimg.org
Secretless AI agents: what xAI-style leaks mean for IAM teams
An editorial by NHIMG, based on Akeyless content, discusses how a developer accidentally exposed an API key in a public GitHub repo for weeks, granting access to private xAI projects, and argues for a 'secretless' AI architecture. The piece emphasizes that static, hardcoded credentials give AI agents and automation durable, reusable access that persists beyond their original task, and offers guidance like eliminating secrets from source-controlled files and issuing short-lived credentials. Details →First reported nhimg.org
Hardware-bound identity for AI agents and the API key problem
An NHIMG analysis of Beyond Identity's argument that AI agents become dangerous when they inherit long-lived, reusable API keys, since a compromised credential lets an attacker act as the agent rather than merely observe it. The piece advocates hardware-bound identity, device binding, and provenance controls as core AI agent governance, citing statistics on exposed credentials and poor rotation/offboarding practices. Details →First reported · updated · 2 reports bleepingcomputer.com
Who Vets AI’s Code? The Scale Challenge Facing Open Source Ingestion
A sponsored ActiveState article explains slopsquatting (AI package hallucination exploitation), where LLM coding assistants suggest non-existent package names that attackers then register on PyPI/npm with malicious payloads to be fetched by CI/CD pipelines. It cites a USENIX Security study across 16 code-generation models and 500,000+ samples, and a 2026 example where a hallucinated npm package name (react-codeshift) spread through forks to over 230 repositories. Details →First reported howtoharden.com
LangChain Hardening Guide
The LangChain Hardening Guide on howtoharden.com is a defensive reference covering security hardening for LangChain, LangSmith, and LangGraph, including SSO/RBAC, SDK CVE patching (CVE-2026-25528 SSRF, CVE-2026-25750 token leak), prompt-injection defense mapped to OWASP LLM Top 10, tracing redaction, sandboxing untrusted code, and agent tool least-privilege. The guide is tiered (L1-L3) and links companion code packs of hardening scripts on GitHub. Details →First reported aifreeup.com
Shadow MCP Servers: The AI Tools Nobody Vetted
An explainer on "shadow MCP servers" argues that developers connect AI assistants to real systems (Google Drive, Notion, browser extensions) via config files that skip normal SaaS vetting, leaving organizations with no inventory of what their AI agents can reach. The piece discusses why standard discovery tools miss these connectors and offers practices for finding and governing them, citing Check Point research on Claude Code MCP vulnerabilities (CVE-2025-59536, CVE-2026-21852). Details →First reported medium.com
How to Secure LLM-Generated Code in Production
A Medium article by Basel Issmail describes an architectural approach to running LLM-generated code in production beside protected health data without trusting each generated line. The piece lays out a threat model — hallucinated APIs, prompt injection altering instructions, tampered artifacts — and argues for limiting the code's authority (no ambient tokens, credentials, or unrestricted HTTP) rather than relying on model behavior as the security boundary. Details →First reported · updated · 3 reports youtube.com
CyberTalks: Data Poisoning Attacks on ML & Agentic AI Systems | Jason Ross |COASP - YouTube
An explainer on AI data poisoning describes how attackers corrupt the data a model learns from, fine-tunes on, or retrieves — including training data, alignment data, and RAG knowledge bases — so a poisoned model behaves as the attacker intends while passing ordinary validation. It distinguishes data poisoning from prompt injection, jailbreaking, evasion, and model poisoning, and notes research (e.g. Carlini et al.'s web-scale poisoning work) showing under 1% poisoned data can measurably change behavior, recommending provenance, access control, monitoring, and rollback as layered defenses. Details →First reported medium.com
From SolarWinds to Slopsquatting: What Five Years Changed in Software Supply Chain Security
A retrospective essay by Manjit Singh reflects on five years of software supply chain security from SolarWinds and Log4Shell to the present, arguing that SBOMs remain unchanged while the surrounding ecosystem has been rebuilt, with AI now positioned as both the biggest new attacker (e.g. slopsquatting, where LLMs hallucinate package names attackers register) and a promising defender. Details →First reported utimaco.com
Data Poisoning: Protect AI from Manipulated Data
A Utimaco blog post discusses data and model poisoning as integrity attacks against RAG and training pipelines, citing OWASP's classification of manipulation of pre-training, fine-tuning, and embedding data. The piece argues for verifying data integrity before inference using cryptographic digital signatures and HSM-protected signing keys, framed around Utimaco's General Purpose HSM offering. Details →First reported · updated · 4 reports cybersecpentesting.com
Tool call poisoning in agentic AI: A technical guide to attack mechanics and defenses
A technical guide explains MCP tool poisoning, an indirect prompt injection attack in which a malicious Model Context Protocol server hides instructions in tool metadata (such as the tool's description field) so that when an AI agent calls the tool, injected instructions enter the LLM context and are treated as trusted input, enabling restricted tool calls, data leakage, or system-prompt bypass. The guide draws on an arXiv STRIDE/DREAD threat model that found tool poisoning to be the most impactful client-side MCP vulnerability across seven major MCP clients, and proposes multi-layered defenses including static metadata analysis, decision-path tracking, behavioral anomaly detection, and user transparency. Details →First reported substack.com
Coding Agent Security: Lessons from Claude Code, Cowork, Codex, and Copilot in the Wild
Ken Huang's survey "Coding Agent Security" reviews documented 2025-2026 incidents involving AI coding agents (Claude Code, Cowork, Codex, Copilot, Amazon Q), including agents that deleted databases, leaked credentials, and merged malicious code after indirect prompt injection via GitHub Issues, source-code comments, or MCP tool results. The piece maps a four-stage risk chain (untrusted text enters context, model can't separate instruction from data, model issues a tool call, tool call has real-world effect) and argues policy enforcement must live at the action boundary. Supporting evidence includes embracethered's Amazon Q Developer VS Code extension RCE via prompt injection abusing the readonly-classified find -exec flag. Details →First reported github.com
mcp-context-forge/SECURITY.md at main · IBM/mcp-context-forge
IBM's mcp-context-forge (MCP Context Forge, an MCP gateway/proxy) publishes a SECURITY.md security policy laying out hardening guidance for deploying the gateway, including keeping the Admin UI development-only, disabling it and unused features in production, requiring authenticated REST-API-only access, and using feature flags to reduce attack surface. Details →First reported · updated · 4 reports deepinspect.ai
MCP Server Security: How Malicious Tools Attack AI Agents | Precursor Security
An analysis of Model Context Protocol (MCP) server security synthesizes research showing publicly exposed, unauthenticated MCP servers nearly tripled from 492 (July 2025) to 1,467 (April 2026) per Trend Micro, that 33% of scanned servers carry critical vulnerabilities (Enkrypt AI), that static long-lived secrets dominate authentication (Astrix), and that 24,008 secrets leaked in MCP config files (GitGuardian). It frames these exposures against attack classes such as tool poisoning, credential theft via prompt injection, lateral movement, and full cloud compromise, referencing the OWASP MCP Top 10. Details →First reported bitsight.com
Shadow AI and the Expanding Attack Surface
A Bitsight analysis argues that Shadow AI—unapproved AI apps, browser extensions, coding assistants, and autonomous agents adopted without security review—silently expands an organization's third-party attack surface. It highlights how the Model Context Protocol (MCP) connects AI applications to repositories, email, and business systems, and how weak authorization, excessive permissions, and untrusted content can create new exploitation pathways for threat actors. Details →First reported bcs.org
How AI is reshaping threats and the steps needed to reduce risk
An opinion piece by Katerina Tasiopoulou (CEO of Threatscene) published by BCS argues that AI is reshaping the cyber threat landscape by expanding the attack surface to include foundation models, training/inference data, prompts, AI APIs, vector databases and automated workflows. The article discusses shadow AI, third-party AI supply-chain dependency and the economic asymmetry between cheap attacker tooling and costly defensive investment, recommending centrally governed, monitored AI security. Details →First reported forcepoint.com
MCP Security: Data Exposure Risks and Controls [2026]
Forcepoint's blog argues that MCP security discussions overlook data exposure risks, noting that a single compromised MCP server can expose every credentialed system it connects to (Salesforce, M365, Slack, code repos, finance DBs). It cites 30+ CVEs filed against MCP servers between January and February 2026, command-injection prevalence, and the postmark-mcp package that silently added a hidden recipient to exfiltrate copies of AI-agent emails. Details →First reported · updated · 5 reports paloaltonetworks.com
What Is Data Poisoning? [Examples & Prevention]
Palo Alto Networks' Cyberpedia entry explains data poisoning against AI/ML systems: how attackers corrupt training data to manipulate model behavior, the different attack types, where poisoning occurs in the pipeline, its distinction from prompt injection, and defensive measures. The page is an evergreen reference/glossary entry rather than a report of a specific incident or new finding. Details →First reported sciencedirect.com
From AI-generated content to agentic action: Security and safety threats in generative AI
A review paper in the Journal of Information and Intelligence titled 'From AI-generated content to agentic action' surveys the security and safety implications as generative AI systems move from producing content to retrieving data, invoking tools, and executing actions through tool chains and external APIs. It analyzes content-level, model-level, and agentic threats alongside countermeasures such as detection, watermarking, alignment, and emerging agentic safeguards, arguing that attack-surface expansion outpaces defensive responses. Details →First reported youtube.com
How LLMs Get Hacked: Top 10 Enterprise AI Attacks and Defenses #aisecurity #cybersecurity
A TedShark Labs YouTube video walks through the top 10 enterprise LLM attack classes — including direct and indirect prompt injection, sensitive information disclosure, supply chain risks (HuggingFace, SBOMs), RAG data/model poisoning, improper output handling (XSS/SSRF), excessive agency, system prompt leakage, embedding weaknesses, hallucination, and unbounded consumption — and recommends defenses like AI gateways, DLP filters, and zero trust controls. Details →First reported github.com
AI & Tech article from HONEYPOTZ | AI Agent Security: Stop Model Exfiltration and API Key Leaks
A HONEYPOTZ blog article titled "AI Agent Security: Stop Model Exfiltration and API Key Leaks" discusses how AI agents expand an organization's security perimeter because they call tools, query data, and can leak model weights or API keys. The piece is an explainer on hardening agentic deployments against model exfiltration and credential leakage. Details →First reported cyberscoop.com
Why transparent AI agents matter more than you think
A CyberScoop op-ed argues that transparent, governed AI agents are essential to defend against prompt injection and other agentic-AI threats, citing Snyk's ToxicSkills audit (36% of Agent Skills contained critical issues) and a Mozilla indirect prompt-injection PoC that spawned a reverse shell via Claude Code. The piece recommends layered defenses including MCP-based governed access, bounded tenant isolation, strict access controls, standardized telemetry, and UEBA/NDR detection. Details →First reported silentrobots.com
MCP list caching and tool poisoning
An analysis of MCP tool poisoning explains how a malicious or compromised MCP server can change a tool's description after the user has approved it, embedding hidden instructions the model follows while the UI still shows the friendly approved name. The piece discusses how the 2026-07-28 MCP spec's new cacheable list fields (ttlMs and cacheScope) could blunt sudden catalog swaps and recommends clients hash approved catalogs, honor TTLs, show full descriptions, and pin versions. Details →First reported · updated · 3 reports darkreading.com
Vibe Coding Security: Risks and Tools | Cycode
Cycode's guide on "vibe coding" security surveys the risks of accepting AI-generated code with little review, cataloging insecure code patterns, hardcoded secrets, hallucinated/malicious dependencies (slopsquatting), weak authentication, over-permissioned coding agents, and prompt injection. It cites studies (including large-scale arXiv analyses) indicating a substantial fraction of AI-generated code contains security vulnerabilities, and promotes Cycode's AI Code Security Assistant for scanning and guardrails. Details →First reported · updated · 43 reports ulad.net
Only 8.5% of MCP Servers Use OAuth — Here's How to Host One Securely on App Service
A Microsoft App Service blog reports that only 8.5% of Model Context Protocol (MCP) servers implement OAuth, leaving the large majority exposed without proper client authentication, and provides guidance on hosting an MCP server securely on Azure App Service with OAuth-based access controls. The piece frames unauthenticated MCP servers — the connectors that bridge AI agents to tools and data — as a widespread security gap and walks through hardening recommendations. Details →How the wire is made
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