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TuxBot v3: Inside an IoT Botnet Framework With LLM
Unit 42 uncovers TuxBot v3, an LLM-assisted IoT botnet framework with 17-architecture builds, Telnet brute-forcing, and DDoS capabilities.
Palo Alto Unit 42 identified TuxBot v3 Evolution, a modular IoT botnet framework derived from AISURU, Wuhan-lineage botnets, and MHDDoS. The C-based bot brute-forces Telnet with 1,496 credential pairs, targets over 30 IoT device families, and communicates with a Go-based C2 over encrypted TCP with multiple fallback mechanisms including DGA, P2P, and DNS TXT. LLM-assisted development left hallucinated crypto implementations and broken exploit modules in the analyzed samples, though roughly 70% of core functionality works. Researchers warn polished production builds likely exist, raising the threat potential.
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You're deploying it wrong! TeamCity, Subversion & Web Deploy part 4: Continuous builds with TeamCity
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A Malicious SIM Card Can Run Attacker Code Inside the Modems Behind Cellular IoT Devices
Researchers showed malicious SIM cards can issue RUN AT commands to execute code on Qualcomm modems, compromising Quectel-based cellular IoT devices like EV chargers.
Researchers at the University of Birmingham and Fuzzware found 9 of 26 tested devices accept SIM proactive commands, including six Qualcomm-based cellular modules, five of them Quectel. They achieved code execution on a commercial Autel EV charger via the Quectel EC25's atfwd_daemon unsafe format string, and demonstrated an irreversible 2G downgrade, modem power-off, and arbitrary file reads via a root TFTP daemon on a Quectel EG25-G. Attacks require a hostile SIM already in the slot or an interposer; no attacks have been reported in the wild. Qualcomm has built a hardened configuration disabling the interface by default and Quectel mitigated the file-access flaw; the paper was presented at USENIX WOOT.
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Multiple ArtraDownloader Variants Used by BITTER to Target Pakistan
BITTER used three ArtraDownloader variants since 2015 to target Pakistan, China, and Saudi Arabia, deploying BitterRAT and exploiting CVE-2017-11882 in one wave.
Palo Alto Unit 42 documents ArtraDownloader, a previously unreported downloader family used by the suspected South Asian group BITTER, with three variants and roughly 80 unique samples dating back to February 2015. Between September 2018 and January 2019, BITTER used spearphishing documents hosted on compromised Pakistani websites to target Pakistan and, for the first time, Saudi Arabia; one RTF sample exploited the EQNEDT vulnerability CVE-2017-11882. The downloader retrieves BitterRAT over HTTP, uses simple registry keys for persistence, and byte-level string obfuscation. Infrastructure overlap with the previously reported MY24 InPage-exploit payload was also observed.
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Patchwork Continues to Deliver BADNEWS to the Indian Subcontinent
Unit 42 details Patchwork APT campaigns against the Indian subcontinent using EPS exploits and an updated BADNEWS backdoor targeting Pakistani military and nuclear interests.
Unit 42 observed the Patchwork group (also known as Dropping Elephant and Monsoon) conducting campaigns against targets in the Indian subcontinent using weaponized documents that exploit CVE-2015-2545 and CVE-2017-0261. The documents deliver an updated BADNEWS backdoor that grants attackers full control of victim machines, using dead drop resolvers on legitimate third-party websites for C2 and HTTP for communications. Lures referenced Pakistan Army promotions, the Pakistan Atomic Energy Commission and the Ministry of the Interior, and in late January 2018 the group shifted from CVE-2017-0261 to the older CVE-2015-2545.
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Hackers Can Hide Malicious AI Commands Inside Normal English to Bypass Security Filters
Check Point's PuzzleMask technique hides malicious prompts in ordinary English that fast gatekeeper models miss but high-reasoning downstream models execute.
Check Point researchers disclosed PuzzleMask, a technique concealing policy-breaking instructions in natural-language prose without encodings or invisible characters. Fast screening models classified all 23 crafted wrappers as safe, while a high-reasoning model recovered and acted on the hidden instruction in 17 of 18 tests (94.4%). The gap stems from capability imbalance between gatekeeper and target models, with defenses including paraphrasing untrusted input, stricter self-referential wording rules, and output/tool-call monitoring.
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