LLM framework automates adversary emulation at 84% success
A new arXiv preprint shows LLMs can read threat reports, generate attack playbooks, and self-correct failures, reaching 84% with Claude Sonnet 4.5.
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A new arXiv preprint shows LLMs can read threat reports, generate attack playbooks, and self-correct failures, reaching 84% with Claude Sonnet 4.5.
Structural priors lift LLM vulnerability recall from 20% to 100% on synthetic benchmarks, but real CVE data exposes a 51-point collapse.
A replay analysis of three LLM agent benchmarks finds the safe partial-run fraction ranges from 15% to over 95%, with no universal shortcut.
A 17 July arXiv preprint shows attackers can weaponise setup docs to trick AI coding agents into installing untrusted dependencies across npm and Cargo.
An arXiv preprint introduces AutoSynthesis, a multi-agent system that turns a clear research question into a full meta-analysis report.
A new arXiv preprint proposes mutable sketches that update user embeddings on-the-fly, cutting data reads to 1.8% and eliminating retrain cycles.
MemPoison benchmark tests 1,227 attacks across 10 model families, finding write-time defenses miss sophisticated memory corruption in LLM agents.
ReBound, a new arXiv preprint, reuses cached query results to answer follow-up questions at reduced or zero additional privacy cost for analysts.
New STOCKTAKE benchmark: LLM agents detect up to 88% of hidden supply-chain failures but two of four models score below a blind baseline on action.
Adversaries can embed hidden instructions in network logs that hijack LLMs used by security teams, with attacks succeeding up to 88.2% of the time.
New arXiv preprint finds offensive AI security agents improve with more compute, but defensive SOC tasks need disciplined tool use over raw reasoning
A July 2026 arXiv preprint tests PAT, a RAG-based system that feeds whole-document context to LLMs for English-to-Spanish translation, with mixed results.