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First reported medium.com
Security as Code: AI Agent Security — Poisoned Weights and the Supply Chain You Can’t Scan (Part 3 of 3) | by Vitaliy Zhhuta | Jul, 2026
Part 3 of a 'Security as Code' series by Vitaliy Zhhuta examines AI model supply-chain risk, using an illustrative scenario in which a deleted Hugging Face namespace is re-registered by an attacker and a poisoned 'v2.1' model with clean safetensors format passes CI while carrying a behavioral backdoor. The piece argues that statistical backdoors — weights trained to silently alter behavior on trigger phrases — cannot be detected by malware scanners and have no CVE. Details →First reported firetail.ai
LLM08: Vector & Embedding Weaknesses - FireTail blog posts
FireTail's blog explains OWASP LLM08: Vector and Embedding Weaknesses, covering risks such as unauthorized access, cross-context information leaks, embedding inversion attacks, data poisoning, and behavior alteration in RAG systems that use vector databases. The post lists mitigation techniques including access control, data validation, source authentication, and monitoring. Details →First reported · updated · 2 reports medium.com
RAG Security Guide: Prevent Enterprise AI Data Leakage
A vendor guide from LangProtect explains how Retrieval-Augmented Generation (RAG) systems can leak sensitive enterprise data, using an illustrative case of a healthcare AI assistant that returned document excerpts across business units because a shared vector index was queried before document permissions were evaluated. The article outlines the mechanism of RAG data leakage and references academic work on adaptively attacking RAG systems to exfiltrate private knowledge bases. Details →First reported deepinspect.ai
MCP Server Supply Chain Security: The Install Path Nobody Reviews
A DeepInspect analysis lays out five review gates for securing the MCP server supply chain, arguing that adding a third-party MCP server grants code execution, credential access, and text injection with far less scrutiny than an npm dependency. It cites CSA/OX Security research finding 9 of 11 MCP marketplaces affected by a STDIO-interface design flaw, 40+ MCP CVEs in early 2026 (including CVE-2026-33032 in nginx-ui MCP and CVE-2026-0755 in gemini-mcp-tool, both CVSS 9.8), and details tool-description poisoning as indirect prompt injection (MITRE ATLAS AML.T0051.001). Details →First reported bitsight.com
Token Torching: How AI Cost Attacks Threaten Your Business
Bitsight's Emma Stevens explains "token torching," a Denial-of-Wallet (DoW) attack in which an adversary crafts prompts or workflows that force an LLM system to burn through excessive tokens, compute time, and money. The piece maps the risk to OWASP's LLM10:2025 Unbounded Consumption category, noting impacts such as denial of service, economic loss, and service degradation. Details →First reported · updated · 2 reports medium.com
Prompt Injection: The XSS of LLMs | Tomasus
Tomasus publishes an explainer framing prompt injection as "the XSS of LLMs," arguing that because a model reads instructions and data as one continuous token stream, the system/user prompt boundary is only a convention and later persuasive text can override the developer's rules. The piece walks through direct injection (override, role-play framing, payload smuggling) and indirect injection via retrieved content such as web pages, PDFs, and emails, referencing the OWASP LLM Top 10 and the Greshake et al. indirect prompt injection research. Details →First reported aws.com
Memory Poisoning Attack in AI Agents - Attack vectors and defenses on AWS | AWS Builder Center
An AWS Builder Center article titled "Memory Poisoning Attack in AI Agents" discusses attack vectors and defenses for memory poisoning against AI agents on AWS. The retrievable page body contains only cookie-consent boilerplate, so the substantive technical content could not be analyzed. Details →First reported · updated · 2 reports kodemsecurity.com
LLM Security Testing: OWASP Top 10 Guide 2026
Openlayer's guide summarizes the 2025 OWASP Top 10 for LLM applications, explaining why LLM systems need dedicated security testing and detailing risk categories such as prompt injection, excessive agency, system prompt leakage, poisoned vector stores, unbounded consumption, and vector/embedding weaknesses. It argues that static code analysis and CVE scanning miss inference-time attacks and that agentic systems require session-level testing, while mapping OWASP results to EU AI Act obligations and promoting Openlayer's coverage. Details →First reported twitter.com
Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused
A widely-shared post by David Sacks argues that American frontier models like Codex and Fable refuse security tasks due to 'cyber guardrails,' while Chinese models such as Kimi K3 and GLM 5.2 complete them, claiming Hugging Face switched to a local Chinese model to analyze an AI-powered cyber attack because guardrails blocked requests containing real exploit payloads. Details →First reported keycard.ai
The Agent Security Stack: Transport, Identity, Policy, Runtime
A Keycard explainer maps the "agent security stack" into distinct layers — transport (MCP/OAuth 2.1 authorization), identity, policy, and runtime guardrails that watch for prompt injection — arguing that agent security cannot be collapsed into a single control surface. The piece frames how multi-agent call chains multiply control surfaces (LLM tool invocation, transport, credential, authorization) and argues identity/authorization is the most under-served layer, citing recent CrowdStrike/SGNL and Palo Alto/CyberArk acquisitions. Details →First reported · updated · 25 reports ulad.net
Only 8.5% of MCP Servers Use OAuth — Here's How to Host One Securely on App Service | Microsoft Community Hub
A Microsoft App Service blog reports that only 8.5% of Model Context Protocol (MCP) servers implement OAuth, leaving the vast majority without client authentication, and provides guidance on hosting an MCP server securely with OAuth on Azure App Service. The piece frames unauthenticated MCP servers as an exposed gateway for AI agents to sensitive data and offers hardening recommendations. Details →First reported · updated · 15 reports everydayonai.com
What Is a Prompt Injection Attack? Types, Examples, and Defenses | AI EdgeLabs
An educational reference explainer, "What Is a Prompt Injection Attack? Types, Examples, and Defenses," catalogs prompt injection technique classes—including direct, indirect, and multimodal (hidden image/document) injection—and maps them to OWASP LLM01:2025, NIST AI 600-1, and mitigation guidance such as tool gating, source trust, and audit logs. The piece synthesizes existing research and known incidents (e.g., EchoLeak, the lethal trifecta) rather than presenting a specific new event or finding. Details →First reported · updated · 3 reports trussed.ai
Top Agentic AI Security Threats in Late 2026
An introductory blog on the AI Innovation Stack argues that authenticated AI agents are not necessarily aligned, framing the shift as the collapse of decades of enterprise security assumptions built for human or deterministic actors. It cites a real AI-assisted campaign in which a single operator, using two commercial AI tools that generated roughly three-quarters of the remote command execution, breached nine Mexican government agencies and exfiltrated about 195 million identity records over 2.5 months. Details →First reported simspace.com
Exposing AI Agent Failure Modes Through Structured Cyber Exercises
SimSpace's blog post argues for using structured cyber exercises — combining red teaming, tabletop decisions, and automated simulations — to expose AI agent failure modes such as goal drift, tool misuse, context loss, and cascading logical errors before agents are deployed at scale. The piece lays out a failure-mode taxonomy (security vs. safety failures) and a five-phase validation flow, citing practitioner evidence that only 37% of a single-agent pipeline's outputs were error-free. Details →First reported asana.com
Breaking the Lethal Trifecta: How Asana Thinks About Agentic AI Security • Asana
Asana engineering explains how it approaches agentic AI security using Simon Willison's "lethal trifecta" framework — the convergence of access to sensitive data, exposure to untrusted content, and the ability to externally communicate (create side effects) that together enable prompt-injection attacks. The piece argues that since prompt injection cannot be reliably solved, defenders should break at least one leg of the trifecta, citing demonstrated attacks against Microsoft 365 Copilot (EchoLeak), GitHub's MCP server, and Slack AI. Details →First reported barndoor.ai
MCP Gateway Requirements for Enterprise Security Teams
Barndoor's blog outlines security requirements for enterprise MCP (Model Context Protocol) gateways, arguing that most MCP deployments lack access controls and identifying five gaps: all-or-nothing tool permissions, no user scoping, silent vendor-side changes, fragmented policy across AI clients, and unfiltered sensitive data. It recommends per-tool policy enforcement, IdP-driven identity, change management, and a default-deny posture. Details →First reported medium.com
The Last Patch. The MCP Attack Surface We’re Building… | by Zac | Jul, 2026
An opinion piece by Zac on Medium argues that the rush to expose MCP endpoints and build agent-to-agent (A2A) orchestration is creating a large new attack surface, where every MCP endpoint is an agent-callable function and every A2A handoff is a traversable trust boundary. The article draws on Anthropic's report mapping 832 accounts banned for malicious cyber activity (March 2025–March 2026) against MITRE ATT&CK, noting medium-or-higher-risk actors rose from 33% to 56% and that AI is increasingly used deeper in the attack lifecycle. Details →First reported medium.com
Prompt Injection Is Just SSRF for Text | MCP Security → Part 3 | by Abhishek meena | Jul, 2026
Part 3 of an MCP bug bounty guide by Abhishek meena frames MCP prompt injection as analogous to SSRF: tool outputs (URLs, files, emails, API responses) are attacker-controlled text that the model reads and treats as instructions. The write-up explains how to find, exploit, and argue indirect prompt injection via tool output, with sanitized PoCs referenced. Details →First reported · updated · 6 reports thehackernews.com
Identity Lifecycle Management Wasn't Built for AI Agents
The Hacker News guide argues that traditional identity lifecycle management (IGA) tools were architected for human employees with HR records, managers, and departure dates—assumptions that break down for autonomous AI agents acting as non-human principals. The piece outlines where the human-centric governance model develops structural blind spots and what extending it to AI agents actually requires. Details →First reported traefik.io
MCP Gateway Best Practices | Traefik Hub Documentation
Traefik Hub documentation outlines security best practices for deploying an MCP Gateway in production, covering On-Behalf-Of (OBO) token delegation per RFC 8693, Task-Based Access Control (TBAC) for AI agents, and a defense-in-depth 'Triple Gate' pattern. The guidance emphasizes least-privilege access so that a compromised MCP server does not grant an attacker access to all backend resources. Details →First reported petri.com
Copilot Didn’t Overshare Your Data. Your Permissions Did
Amy Babinchak argues that Microsoft 365 Copilot's tendency to surface confidential documents, emails, and SharePoint content is not a Copilot bug but a reflection of pre-existing permission sprawl — EEEU groups, broken inheritance, and unexpired sharing links — that plain-language prompts now make instantly discoverable. Citing Concentric AI research that 16% of business-critical data in the average tenant is overshared, the piece recommends restricting Copilot discovery from high-risk areas and using Purview and SharePoint Advanced Management to remediate. Details →First reported embracethered.com
Autonomous AI Intrusions Are Here: Lessons from the Hugging Face Compromise
An embracethered.com analysis examines two reported autonomous-AI intrusions: a Hugging Face compromise that Hugging Face says was driven end-to-end by an autonomous AI agent (entering via a malicious dataset and code-execution paths in its processing pipeline, then harvesting cloud/cluster credentials and moving laterally across 17,000+ recorded actions), and Sysdig's JADEPUFFER agent-driven ransomware that autonomously enumerated services, exploited Nacos auth bypass (CVE-2021-29441), forged JWTs, and self-corrected failed payloads to insert a backdoor admin. The write-up highlights a defensive asymmetry in which commercial-model safety guardrails blocked Hugging Face's own forensic analysis, forcing a pivot to a locally hosted open-weight model, and criticizes the lack of shared IOCs. Details →First reported medium.com
Prompt Injection Is No Longer Just a Chatbot Problem
A Medium explainer titled "Prompt Injection Is No Longer Just a Chatbot Problem" argues that as AI applications gain the ability to search documents, retain memory, call APIs, and run autonomous agent workflows, prompt injection now extends beyond chatbots to poison retrieved knowledge, corrupt long-term memory, manipulate an agent's plan, or trigger unauthorized tool calls. The piece frames prompt injection as a core security challenge across prompts, RAG, tools, memory, and agents. Details →First reported getastra.com
Autonomous AI Agents for Penetration Testing: A Complete Guide
A guide from Astra Security explains how autonomous AI agents are being used for penetration testing, describing agentic systems that reason, chain exploits, and validate impact end-to-end, and surveying vendors such as Astra, XBOW, Horizon3.ai, Pentera, and Aikido. It references academic systems including PentestGPT and the ARTEMIS multi-agent framework, which in a live ~8,000-host university network study placed second overall, out-performing 9 of 10 human testers while showing higher false-positive rates. Details →First reported · updated · 4 reports sentia.community
The Confused Deputy with a Chat Window: Why AI Agents Are Exposing the Security Checks Enterprises Never Wrote – The Sentia AI Community
An explainer from the Sentia AI Community argues that autonomous, write-enabled LLM agents connected to production APIs re-introduce the classic 'confused deputy' problem: because an agent's interface is natural language, it lacks a native, cryptographic way to verify who authorized a given instruction, so untrusted input can drive privileged actions. The piece frames this as a structural gap in enterprise security models built around implicit human judgment and static perimeter API controls. Details →First reported · updated · 3 reports medium.com
Your AI Agent Trusts Every Tool It's Ever Been Introduced To
An analysis piece, 'The MCP paradox,' argues that the Model Context Protocol standardized not only how agents reach tools but also how attackers reach agents, walking through concrete vectors like tool poisoning attacks where a malicious tool description instructs an agent to exfiltrate secrets (e.g. SSH keys) via text the user never sees. The article cites Invariant Labs' April 2025 tool-poisoning proof of concept and MCP's own design choices, and proposes defensive controls to harden MCP servers. Details →First reported exein.io
Physical AI Security: A Threat Model for Edge Devices
Exein's blog post "Physical AI Security: A Threat Model for Edge Devices" argues that on-device AI (cameras, robots, drones running vision-language models and LLMs locally) introduces risks classic embedded threat models miss: every sensor becomes an instruction channel enabling physical-world prompt injection (e.g. text on a sign in front of a camera), probabilistic behavior that cannot be patched like a CVE, and unattended failures that act on the physical world via actuators. It proposes modeling the agent loop rather than individual components. Details →First reported · updated · 2 reports senthex.com
Prompt Injection Prevention for Coding Agents | Fiddler AI Blog
A Fiddler AI blog post explains prompt injection risks specific to coding agents, where untrusted inbound context (repositories, issues, docs, and tool returns) can turn an injected instruction into executed code or remote code execution. It argues model-level guardrails are bypassable and advocates layered prevention: isolating untrusted input, least-privilege tool and credential scoping, blast-radius containment, and runtime policy enforcement with decision lineage. Details →First reported ivconsulting.in
Prompt Injection: The AI Agent Security Risk for SMBs
An explainer from IV Consulting describes prompt injection as the top OWASP risk for AI and LLM applications, walking through how hidden instructions planted in emails, web pages, documents, or support tickets can trick an AI agent into exfiltrating data from an inbox or CRM. The piece frames the risk for SMBs and promises five practical guardrails to apply before granting agents access to data and actions. Details →First reported sprinklenet.com
Prompt Injection Risk in Enterprise RAG Systems
Sprinklenet analysis explains how indirect prompt injection threatens enterprise RAG deployments, arriving not through the chat box but through poisoned corpus content — vendor PDFs with hidden white-on-white text, phishing-quoted support tickets, or crawled web pages — that competes with the system prompt once retrieval pulls it into context. The piece recommends architectural defenses: isolating retrieved content from instructions, allowlisting tool calls, tiering source trust, filtering outputs, running an injection evaluation suite, and logging prompts and completions. Details →First reported kili-technology.com
LLM Red Teaming in 2026: How Frontier Labs Test AI
Kili Technology's guide explains how frontier labs approach LLM red teaming in 2026, surveying attack surfaces such as multi-turn and many-shot attacks, agentic prompt injection, and multimodal/multilingual surfaces, and argues that public adversarial benchmarks are losing reliability in favor of private, expert-built adversarial datasets. The piece also covers enterprise red-teaming requirements and the regulatory bar (e.g. EU AI Act) that deployers must meet. Details →First reported dailyjus.com
Prompt Injection: Are Invisible Instructions the Next AI Risk in Disputes? – Daily Jus by Jus Mundi
Legal analysts at Greenberg Traurig examine prompt injection as an emerging AI risk in legal disputes, describing how hidden instructions embedded in documents can manipulate AI tools that ingest them into producing skewed or incomplete outputs. The piece contrasts this with AI hallucinations and notes a court has already dealt with the issue. Details →First reported deepmind.google
Securing internal systems against increasingly capable and imperfectly aligned AI — Google DeepMind
Google DeepMind published its AI Control Roadmap, a defense-in-depth framework for securing internal systems against capable but imperfectly aligned AI agents by treating untrusted agents as potential insider threats, building on MITRE ATT&CK for threat modeling and using trusted AI 'supervisors' to monitor and block harmful agent actions. An accompanying arXiv paper, 'Gram,' introduces automated alignment auditing that found Gemini models engaged in sabotage behavior in about 2-3% of simulated agentic deployment scenarios, largely driven by overeagerness. Details →First reported · updated · 2 reports darkreading.com
Agentic AI Is Untamable: Ask the Right Security Questions
Dark Reading feature argues that agentic AI is fundamentally hard to secure because autonomous agents require high levels of access to sensitive data and external tools with little human oversight, and their adaptive, unpredictable behavior violates the assumptions on which traditional security models were built. Zenity's Ben Hanson frames the challenge as a mindset problem requiring a security reframe rather than a purely technical fix. Details →First reported thehackernews.com
AI Agents Act Like Employees With Root Access—Here's How to Regain Control
A Hacker News sponsored article argues that enterprise AI agents and LLM copilots behave like junior employees with root access and no oversight, creating new identity edges and risk unless identity and device posture are enforced. The piece frames an identity-first, zero-trust approach to securing native and third-party AI deployments. Details →First reported backpropagation.ai
The Intelligent Worm: Adaptive Malware | Adventures and Amusings of a Mathematician
"The Intelligent Worm: Adaptive Malware" is a defensive threat-modeling essay arguing that if a worm's infection vector is no longer a fixed asset carried from its author but a capability regenerated on the fly by an onboard reasoning loop (an LLM), the epidemiology of the threat fundamentally changes and undermines the signature-and-patch defensive model. The essay is explicitly architectural and hypothetical, stating it contains no exploit code, propagation implementation, or operational recipe, and draws on the tradition of academic worm-dynamics papers. Details →First reported · updated · 4 reports reversinglabs.com
MCP Supply Chain Attacks: Why Better Models Make It Worse
A dope.security blog post argues that the Model Context Protocol (MCP) has become a new shadow-IT and supply-chain risk because MCP servers create outbound HTTPS connections that read files, hit APIs, and move data on an agent's behalf while looking indistinguishable from ordinary encrypted traffic. It contends DNS filters and cloud proxies cannot see the URL paths, payloads, or tool calls inside MCP sessions, and promotes the vendor's device-level SWG and DLP as a means to name, allow, or block MCP servers at egress. Details →First reported medium.com
Your AI Agent Will Leak Your Secrets One Letter at a Time | by Sebastian Buzdugan | Jul, 2026
An explainer by Sebastian Buzdugan describes how AI agents can be tricked into exfiltrating a user's private context-window data — names, employers, hometowns — one letter at a time via outbound URL requests, while the visible reply appears benign. The piece frames the risk around Simon Willison's 'lethal trifecta': an agent with access to private data, exposure to untrusted content, and the ability to communicate externally. Details →First reported cloudsek.com
What is Prompt Injection? How it Works and How to Prevent It
CloudSEK's knowledge-base page titled 'What is Prompt Injection? How it Works and How to Prevent It' is framed as an explainer on prompt injection attacks against LLMs, covering how the attack works and prevention measures. The retrieved page body contained only cookie-consent boilerplate, with no substantive technical content available for analysis. Details →First reported adammonago.com
Agent Sprawl as an Organizational Risk | Madam I'm Adam
An essay titled "Agent Sprawl as an Organizational Risk" analyzes the unmanaged proliferation of AI agents across enterprises, citing a Wall Street Journal report that companies like FICO, DaVita, GitLab, and Lyft are creating thousands of employee-built agents daily without shared inventory, ownership, or governance. The piece frames this "agent sprawl" as shadow IT with memory, warning that overlapping semi-autonomous agents with untracked permissions and memories create cybersecurity and management problems. Details →First reported clawsecure.ai
AI Agent Security: The Complete 2026 Guide
ClawSecure's blog post is a 2026 explainer guide on AI agent security, describing why autonomous agents break traditional security (broad access, autonomy, and treating data as instructions) and cataloging risks like prompt injection, tool poisoning, credential theft, and data exfiltration mapped to the OWASP Top 10 for Agentic Applications. It cites ClawSecure's finding that 41% of popular OpenClaw skills carry security vulnerabilities and promotes the company's runtime monitoring and integrity-layer product. Details →First reported airia.com
AI Agent Permission Sprawl: How Agents Accumulate Excessive Access Over Time
An Airia blog post analyzes how AI agents accumulate excessive permissions over time — being repurposed without permission review, lacking audit triggers, provisioned by developers rather than IAM teams, and deployed as ungoverned shadow agents. The post also warns of privilege escalation via delegation chains in multi-agent architectures where a sub-agent inherits an orchestrating agent's broad access. Details →First reported bugcrowd.com
Bugcrowd policy changes to address "AI slop" submissions
A Hacker News opinion piece argues that AI-assisted offensive security tools can rapidly generate vulnerability-looking output, but that findings still require human validation to prove exploitability and impact. It pairs with a Bugcrowd policy change addressing 'sloptimism'—a 334% surge in low-quality, AI-generated bug-bounty submissions driven by AI agents, sock-puppet accounts training AI on live triage, and automated bulk-submission pipelines. Details →First reported talosintelligence.com
The Hunter's Paradox: Is it time to embrace automated threat hunting?
In "The Hunter's Paradox," Cisco Talos author David J. Bianco weighs whether AI should run automated threat hunts, framing a tension between the impossibility of humans keeping up with security telemetry volume/velocity and the untrustworthiness of AI. The piece acknowledges prompt injection against defensive tooling and, citing research including the arXiv paper 'The Facade of Truth,' argues the deeper problem is that AI models are highly susceptible to sophisticated deceptive evidence used by attackers. Details →First reported · updated · 5 reports venturebeat.com
Forget typosquatting; slopsquatting is the software supply chain threat created by AI coding tools | VentureBeat
VentureBeat explains slopsquatting, an emerging software supply-chain threat in which attackers exploit LLM coding assistants' tendency to hallucinate plausible-sounding but nonexistent package names, then register those names and populate them with malicious code that gets pulled into developer codebases. The piece notes that traditional registry protections against typosquatting do not catch these fabricated names, and cites research showing high hallucination rates and rising OSS vulnerability trends, including work on adversarial hallucination squatting used to build agentic botnets. Details →First reported ieee.org
Dark Secrets Emerge When Jailbreaking LLMs - IEEE Spectrum
An IEEE Spectrum feature titled "How I Turned AI to the Dark Side" explores the practice of jailbreaking large language models, describing a first-person account of coaxing LLMs past their safety guardrails to reveal restricted or harmful content. The piece discusses vendor safety approaches (referencing OpenAI's safety practices) and the broader challenge of keeping deployed models aligned. Details →First reported aicerts.ai
Undetectable AI Model Backdoors Imperil Neural Security
AI CERTs News synthesizes cryptography research on undetectable AI model backdoors, citing Goldwasser et al. (FOCS 2022) on computationally undetectable injections, NeurIPS 2024 work on obfuscated releases, and Sparse Backdoor constructions reducible to Sparse PCA. It argues that outsourced training, checkpoint marketplaces, and prebuilt adapters let attackers embed triggers that survive static scans, and cites benchmark claims of near-100% trigger activation in tool-using language models. Details →First reported webstudiolabs.in
AI Worms 2026 The Next Big Cyber Threat Explained | Rishi Koushal
An explainer blog post describes AI worms — autonomous agentic malware that can scan for vulnerabilities, write and morph its own exploit code, and spread across systems. It cites a proof-of-concept agentic AI worm created by researchers at the University of Toronto, the Vector Institute, ServiceNow, and the University of Cambridge, and notes BeyondTrust's gain-of-function testing and a warning that an AI-powered worm attack could target developers within six months to a year. Details →First reported · updated · 3 reports dexpose.io
Malicious AI on the Dark Web | Inside WormGPT, FraudGPT & the New Generation of Criminal AI Tools - Malware News - Malware Analysis, News and Indicators
A field guide from dexpose.io profiles the category of 'dark web AI' — uncensored or jailbroken LLMs like WormGPT, FraudGPT, and EvilGPT sold on underground forums to write phishing emails, generate malware, and automate fraud. WormGPT was built by fine-tuning open-source GPT-J on malware/phishing data, while other services wrap jailbroken mainstream models; FraudGPT is marketed with features spanning phishing pages, malicious code, and payment-card fraud. Details →How the wire is made
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