42
60
60
45
42
42
30
60
September 2025 CVE Landscape
60
APT36 Suspected in PATCHCORD Espionage Campaign Using Google Sheets C2
Acronis links the PATCHCORD espionage campaign targeting Afghan telecom and South Asian critical infrastructure to APT36 with moderate confidence.
Acronis Threat Research Unit documented PATCHCORD, a previously undocumented C/C++ backdoor delivered via fake VPN installers impersonating Afghan Telecom (AFTEL), which hijacks Edge, Chrome and Firefox shortcuts for persistence and executes in-memory shellcode. A related Go implant, SHEETCORD, abuses the Google Sheets API for per-victim C2 and was distributed via a domain impersonating India's National Informatics Centre, while a third tool, HACKERAI C2 Agent, uses GitHub Gists and shows signs of LLM-assisted coding. An exposed staging server revealed SuperShell, RAT frameworks, credential-harvesting tools and OpenSSH exploit code, and the researchers attribute the activity to APT36/Transparent Tribe at moderate confidence.
72
30
55
42
47
When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications
CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.
The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.
28
60
35
55
42
30
30
42
42
60
60
45
55
57
Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?
Six frontier models from OpenAI, Anthropic, xAI, and Google DeepMind converge on one imagined successor architecture when asked under a school-audience framing.
Researchers ran ten independent sessions per model type across six frontier models using a three-stage prompt sequence progressing to a full ASCII backbone architecture. Under school-audience framing, responses repeatedly converged on a shared motif including persistent latent state, adaptive computation, memory, specialist routing, verification, and stopping control, while control runs without the framing produced heterogeneous responses. A GPT-5.6 Sol output closely overlapped an architecture independently sketched by GPT-6 Astra, raising questions about shared design priors or motif propagation between model families. The paper coins 'epistemic jailbreak' for the observed loss of provenance discipline as prompt specificity increases.
35
60
30
57
60
30
42
57
42
57
60
50