Hackers Use Claude and GPT-Powered Tools to Help Breach Government and Financial Networks
Unit 42 links two Latin America campaigns where operators used Claude and GPT-4.1 during intrusions against government and financial targets.
Palo Alto Networks Unit 42 identified two activity clusters, CL-CRI-1131 and CL-CRI-1163, tied by shared SOCKS5 relay infrastructure and use of large language models during operations. The Mexican cluster targeted a transportation organization, federal ministries and water utilities in Mexico and Ecuador, while the Brazilian cluster used resume-themed phishing, custom remote-access Trojans and SockTz SOCKS5 tunneling against financial organizations. An exposed self-hosted NextChat interface on attacker infrastructure led researchers to assess operators used Claude and GPT-4.1 to generate workaround scripts and troubleshoot execution failures. Unit 42 noted AI reduced time needed to troubleshoot intrusions after initial access, rather than replacing the attacker.
Mirai Variant V3G4 Targets IoT Devices
Unit 42 tracked the Mirai variant V3G4 exploiting 13 vulnerabilities across IoT devices and servers from July-December 2022 to build a DDoS botnet.
From July to December 2022, Unit 42 observed three campaigns spreading V3G4, a Mirai botnet variant, by exploiting 13 vulnerabilities, including CVE-2022-26134 in Atlassian Confluence and CVE-2019-15107 in Webmin. The campaigns shared the same C2 domains containing the string 8xl9, nearly identical shell script downloaders, and the same XOR keys, suggesting a single threat actor. The malware brute-forces telnet and SSH credentials, terminates rival botnet processes via a stop list, and receives DDoS commands from its C2. Compromised servers and networking devices are absorbed into the botnet for further attacks.
Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America
Unit 42 documents two AI-assisted intrusion campaigns against Latin American government, utility, and financial organizations using LLM-orchestrated tooling.
Palo Alto Networks Unit 42 tracks two ongoing intrusion clusters, CL-CRI-1131 (Mexican transportation, federal ministries, municipal water utilities) and CL-CRI-1163 (Brazilian financial sector), both using living-off-the-land techniques, SOCKS5 relays, and custom RATs. The attackers appear to orchestrate operations via commercial LLMs like Claude and GPT-4.1, evidenced by iterative batch scripts and AI-generated tunneling tool naming. The Mexican campaign (also reported as Operation Escaneo by CloudSEK) exfiltrated sensitive data via dynamic-DNS infrastructure with rotated multi-SAN TLS certificates between February and June 2026. This signals broader adoption of AI-enhanced operations by diverse threat groups in the region.
DPRK APTs: Ted backdoor and curlRAT target South Korean media and automotive sectors
Rapid7 uncovered a DPRK-linked Linux toolkit using a HAProxy-embedded ted backdoor, SSH keylogger, and curlRAT against South Korean media and automotive firms.
Rapid7 Labs identified a previously undocumented framework attributed with medium confidence to DPRK actors, targeting South Korean automotive and media organizations likely since early 2025. The toolkit embeds a backdoor compiled into HAProxy 2.8.12 using its filter API, plus trojanized crond, agetty, atd, sshd, and polkitd, an SSH keylogger storing credentials under /var/lib/sshd/, and a curl-based RAT with a watchdog thread. It enables remote command execution, malicious script injection into served webpages (a watering-hole loop), credential harvesting, and long-term surveillance. Hardcoded C2s are associated with APT37 via ThreatFox, and exposed groupware portals and mail servers align with Kimsuky tradecraft; the initial access vector and any CVE remain unconfirmed.
Hackers Weaponize AI Safety Guardrails to Hide Malware From LLM-Powered Security Scanners
ESET says Russia-aligned actor UAC-0099 hid guardrail-triggering comments in VBScript to derail LLM-based malware scanners in Ukraine.
ESET researchers linked a technique named GuardBreaker to Russia-aligned threat actor UAC-0099 during an attack against an organization in Ukraine. The group embedded a safety-sensitive, weapon-related request in a VBScript comment so an LLM-powered analysis tool might interpret it as an instruction and refuse or truncate analysis before reaching the malicious code. The VBScript downloaded MATCHBOIL, a C#-based loader used by the group alongside MATCHWOK and DRAGSTARE. OWASP guidance recommends treating code comments and metadata as untrusted input, sanitizing it, and never treating an LLM refusal as a clean verdict.
Multiple Chinese hacking groups seen using identical Chrome zero-day exploit
Four China-linked espionage groups share identical BlueMoon Chrome zero-day exploit kit targeting US defense contractors and Asian government agencies.
Proofpoint identified at least four Chinese-aligned espionage groups (TA412/RedBravo, UNK_LateNight, UNK_DoubleCheck, UNK_QuietRacket) using an identical Chrome zero-day exploit kit dubbed BlueMoon in late August through this week. Targets include US defense contractors, NGOs, mining companies, and Southeast Asian government agencies. The exploit chains a Chromium patch-gap vulnerability with a Windows flaw, delivering malware such as ShadowPad and a fake Gemini browser extension backdoor, with possible AI-assisted exploit development.
Android Car Malware Spreads Through Built
Kaspersky found MoYu Group malware infecting DoFun Android car head units via firmware updaters, enabling ad fraud and proxy botnet operations.
Kaspersky discovered in June 2026 the first documented malware specifically infecting Android-based car head units, spread through the built-in updater (TWCore) of DoFun head unit firmware via a dropper dubbed JarService. The multi-stage implant supports nine commands enabling unwanted ads, ad fraud, and additional module downloads, and installs the zhima reverse proxy module. The campaign is attributed with high confidence to the MoYu Group behind the BADBOX ad fraud and residential proxy scheme; the distribution issue was fixed after responsible disclosure.
The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)
An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway
A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.