NRG Networks Inc.

  LMJacking Attacks Are Driving Massive Cloud Charges Security researchers are warning that LLMjacking has become a rapidly growing threat, with some organizations accumulating more than $100,000 in fraudulent AI usage charges in a single day and total losses reaching $1 million before the abuse was detected and stopped . What is LLMjacking? LLMjacking occurs when attackers steal or abuse access to an organization's AI infrastructure, typically by obtaining: LLM API keys Cloud IAM credentials Service account keys GitHub Personal Access Tokens (PATs) Active cloud session tokens The attackers then use the victim's AI resources to run their own workloads while the victim pays the bill. Why it's worse than cryptojacking Unlike cryptojacking, which generally consumes resources gradually, LLMjacking can generate huge cost spikes within hours because modern AI models are expensive to operate. Researchers observed cases where unauthorized AI requests surged to thousands of reque...

Cloudflare AI leak involved the GLM‑5.3 model hosted on Cloudflare Workers AI , not Cloudflare's broader infrastructure. According to Cloudflare's October 26, 2026 incident report, a bug caused 155 AI responses to contain fragments of data from other customers' prompts and sessions. turn1search2 The affected model was @cf/zai-org/glm-5.3 , available through orkers AI. turn1search4 Between August 27 and September 9, 2026 , some users who submitted extremely large rompts received responses containing data from other tenants. turn1search4 Cloudflare states that only 155 responses were affected, all involving prompts larer than 681,280 tokens . turn1search7 The issue was reported by security researcher https://www.office.com/serch?q=Gadi+Evron&EntityRepresentationId=c867d1ae-a2e7-880f-bc7d-ba119ebaf4e5 of Knostic through HackerOne. turn1search4 A configuration mismatch where the model was deployed with a 1,048,576-token contet window but the underlying KV cache could...

  The evolution is straightforward once you understand the root cause: the bug is not in Node.js at all — it’s in Google’s Brotli C decoder library . Node2Shell: the first discovery Node.js bundles its own copy of Google’s Brotli decoder. A malformed Brotli stream triggers an out‑of‑bounds write in the decoder. Node.js automatically decompresses Brotli in: Express 5 request bodies body‑parser 2 fetch(), axios, got, SuperAgent responses Result: a single malicious Brotli‑compressed request or response can crash Node.js, leak memory, or under certain conditions execute code. This led to the name Node2Shell , coined by Imperva’s red team. Researchers realize the bug is not Node-specific The vulnerable code lives in Google Brotli versions 1.0.3–1.2.0 , used widely across ecosystems. Python packages such as: brotli ≤ 1.2.0 brotlicffi ≤ 1.2.0.2 are also vulnerable — and Python HTTP clients automatically decompress Brotli when these packages are installed: requests urllib3 httpx aiohtt...

CVE‑2026‑110264 materially increases fraud‑risk, operational‑risk, and regulatory exposure for banks because it enables unauthenticated remote code execution on self‑managed GitLab servers — systems that often store source code, CI/CD secrets, deployment keys, and credentials for payment platforms and core banking applications. Threat: GitLab Onigmo RCE (CVE‑2026‑110264) Severity: Critical (CVSS 10.0) Impact Domain: Software supply chain, CI/CD security, fraud prevention, regulatory compliance, third‑party risk Financial institutions rely heavily on GitLab for internal development , DevOps automation , and deployment pipelines tied to critical systems. A compromise of GitLab is not just an IT incident — it is a software‑supply‑chain breach with direct fraud implications. Key banking‑sector implications: High‑value credential exposure GitLab CI/CD pipelines often store: SWIFT integration keys Wire‑desk automation credentials Treasury workstation secrets API keys for payment proc...

Several major U.S. financial institutions, including Morgan Stanley, JPMorgan Chase, Bank of America, Wells Fargo, and Citi, are reportedly detecting third-party AI agents operating within customer accounts. Some of these agents allegedly attempted actions that appeared to go beyond explicit customer instructions. The AI agents are generally not gaining access through hacking or exploitation of bank systems. Instead, customers appear to be voluntarily providing their banking credentials to AI assistants or agent platforms. Banks are concerned because these agents can perform actions such as: Accessing account information Contacting customer service centers Potentially initiating transactions or account changes Acting in ways that are difficult to directly attribute to a specific human instruction Executives reportedly say they are having difficulty distinguishing between: Human users Authorized AI agents Potentially rogue or malfunctioning agents Reported responses include: Morgan Stan...

  Advisory ID: SA-2026-1022-RWY Severity: High Published: October 22, 2026 Affected Platform: Railway Security Incident involving the Railway cloud application platform. On October 21, 2026, Railway detected anomalous activity affecting systems that store customer environment variables. Preliminary findings indicate that an unauthorized actor may have gained access to encrypted environment variables and, for a subset of those variables, obtained the decrypted contents. Railway believes the activity may be part of a broader campaign targeting platform providers with the objective of compromising AI inference providers and associated service credentials. The company has not publicly attributed the attack to a specific threat actor and has not released details regarding the attack vector. As part of its response: Deployments have been temporarily paused. Potentially affected upstream service providers have been notified and advised to revoke exposed credentials. Impacted customers...