LABScon26 Replay | Let’s Get Physical: Why Cybersecurity Needs Hardware-Based Security to Survive AI
Adyen’s Dino Dai Zovi shows how hardware security proven in payments can contain AI-driven attacks, even when vulnerabilities cannot be patched.
SentinelLabs 10
Covers: Malware
VerifiedIDA | Making LLM-Driven Reverse Engineering Inspectable, Continuable and Reusable
LLM reverse engineering scales only if analysis stays inspectable. A new standard and its open-source IDA Pro implementation tie annotations to evidence.
Defrost and Disrupt | An Assessment of Europe’s Refrigeration Attacks
Store fridges and freezers warmed overnight in six countries on November 2-3. Russian-aligned groups are credible suspects, but attribution remains open.
LABScon26 Replay | Keynote: A Sober Analysis of AI Impact
Harvard Professor James Mickens weighs the promises of AI against its potential harms, arguing for hardware-based mechanisms to isolate frontier models.
Don’t Call Us, We’ll Call Your APIs | Trader Traitor Backdoors Resurface on Victim With No Crypto Ties
North Korean operators built a foothold on a DevOps engineer's Mac in a campaign whose job interview lures deliver malware via Terraform lock files.
Agents at Large | Tracing Illicit OpenAI Agent Activity on Hugging Face
Two Hugging Face accounts reveal that OpenAI's agents staged relay code, internal probes and ChatGPT account registration beyond the published timeline.
The Model Is the Malware | What Four Agentic Intrusions Tell Defenders
OpenAI, Anthropic and Meta disclosed agents reaching external systems. The tools didn't matter, and that changes the playbook for investigating intrusions.
Sol Searching | Can Frontier Models Tackle Autonomous Long-Horizon Malware Analysis?
A real-world benchmark tests whether powerful AI models can keep an investigation trustworthy when new evidence invalidates their conclusions.
Iran War Cyber Threat Landscape | A Midyear Assessment on What Matters
In April, SentinelLABS’ Tom Hegel published an initial assessment of the first five weeks of the conflict. Three months later, the evidence supports refinement.
Context Engineering | Compaction & Agent Memory for Automated Malware Analysis
Compaction cut input tokens 86% across long-running agent evals with no quality loss. Context discipline matters as much as model selection.