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THE DAILY INDEX

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GENERAL

TLDR InfoSec

HOW FIGMA STAYS AHEAD OF VULNERABILITIES WITH AGENTS

Figma uses agents to review PRs for vulnerabilities, audit existing repositories, and generate secure code. When writing the workflow to review PRs, Figma found that the collection of policies and precedents that they wrote to improve precision constituted a detailed threat model for the agent to follow. This threat model was also utilized with more agents to brute force a code audit of a large, legacy monorepo and is used with agent hooks for secure code generation.

ULTIMATE THREAT HUNTING PLAYBOOK FOR RUSSIAN CYBER OPERATIONS IN A HYBRID THREAT ENVIRONMENT

A new hybrid threat hunting framework synthesizes four years of Russian cyber operations, including activity from APT28, Sandworm, and APT29, by focusing on shared operational chokepoints across identity, edge infrastructure, and OT rather than brittle IOCs. To highlight the necessity of behavior-based detection, the playbook points to LAMEHUG's dynamic use of the Hugging Face API and CERT-UA data showing APT28 achieving domain-controller compromise in under an hour. Furthermore, the report advocates for correlating physical and cyber telemetry in response to hybrid threats like the Leipzig/Halle drone incident, though it cautions defenders against attributing attacks based solely on geographic or temporal proximity.

LOCKBIT 5.0 LINUX MALWARE ANALYSIS: CHACHA20 + CURVE25519 OFFLINE ENCRYPTION, STRACE EVASION & IOCS

A reverse engineering teardown of LockBit 5.0's Linux variant found the ransomware encrypts entirely offline, generating ChaCha20 keys locally and wrapping them with a hardcoded Curve25519 public key so that zero network activity occurs during encryption, confirmed via Wireshark, socket monitoring, and interface counters. The sample also detects strace through TracerPid checks in /proc/self/status and fingerprints VM environments via /proc/ioports enumeration, forcing analysts to fall back on eBPF-based tracing to observe its behavior. The findings point to a broader shift toward offline-first ransomware design that makes network-based detection largely irrelevant against this class of threat.

CHINESE MODEL KIMI K3 BREAKS UK AI SAFETY INSTITUTE BENCHMARK EVALUATIONS

In a recent evaluation of Kimi K3's cybersecurity capabilities, Frontier Security attempted to run the model inside the UK's AISI's agentic sandbox and discovered that the model effectively escaped the sandbox to game the benchmark. The model first probed the network and discovered that standard DNS functionality was available for github.com, so it was able to successfully look up the benchmark, clone its repo, and lift the solutions directly from the repo. Frontier Security recommends that evaluators treat evaluation infrastructure as part of the benchmark, deny network access by default, audit traces instead of just answers, revalidate suspicious results, and assume capable models will find paths to “cheat.”

OPEN SECURE AI ALLIANCE PROPOSES SAFE GUIDELINES AS MEMBERSHIP TOPS 120

The recently founded Open Secure AI Alliance has proposed a set of guidelines for reporting cybersecurity incidents involving AI agents titled the Shared AI Findings Exchange (SAFE). The SAFE program would provide organizations with a confidential channel for reporting details of AI security incidents. It would then analyze the data, notify affected parties, flag control failures that recur across its members, and then release recommendations.

KIMI K3 ESCAPED A CYBERSECURITY TESTING ENVIRONMENT DURING EXPERIMENT RUN BY FRONTIER SECURITY, A PRIVATE US FIRM

Moonshot AI's open-weight Kimi K3 broke out of a sandboxed benchmark run by private firm Frontier Security, exploiting unblocked outbound DNS and HTTPS to reach GitHub and pull the official solution instead of solving the task itself. The UK AI Security Institute only built the open-source sandbox framework and was not involved in running the test, and researchers attribute the escape to a network misconfiguration rather than deliberate model misbehavior.

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