Hopper (Product): news timeline & CVEs

REA Tool Connects Claude Code and Cursor to Ghidra and IDA Pro to Reverse Engineer Anything

Open-source REA connects Claude Code and Cursor to Ghidra and IDA for evidence-backed reverse engineering.

REA (Reverse Engineer Anything) is an open-source bridge that lets AI coding agents query Ghidra, IDA Pro, and Hopper through a local Model Context Protocol server. It covers native binaries plus JavaScript, Electron, .NET, Android packages, firmware, and selected runtime activity, returning pseudocode, assembly, strings, and references with stated limits. Setup via npx supports Claude Code, Cursor, Codex, Gemini CLI, and Grok Build. A project-reported DX-Ball demo matched 63 compiled function bytes and passed 3,205 tests; findings sent to the agent remain subject to the model provider’s data policy.

Cyber Security News08:08 UTC · 12h agoTools4· 2 reads

Hopper: Bounded-Memory Collaborative Debiasing for Byzantine-Tolerant Peer Sampling

Researchers propose Hopper, a bounded-memory debiasing protocol that protects Byzantine-tolerant peer sampling from delayed balanced attacks defeating cumulative counters.

The paper shows that frequency-aware debiasing based on cumulative per-identifier counts fails under a delayed balanced attack, where a long benign prefix masks a subsequent adversarial frequency shift. Hopper introduces a bounded-memory debiasing protocol using BitMatcher as the stream estimator, plus BMDecay, a saturation-triggered decay and reconstruction mechanism keeping frequency signal fresh. It also supports authenticated fingerprint-aware trusted collaboration with role-specific debiasing. Experiments show faster recovery from delayed attacks and debiasing comparable to non-debiasing baselines under a fixed memory budget, with a re-identification trade-off at high trusted-node densities.

arXiv cs.CRSep 17, 08:40 UTC · 23d agoResearch

Hopper

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REA Tool Connects Claude Code and Cursor to Ghidra and IDA Pro to Reverse Engineer Anything

Hopper: Bounded-Memory Collaborative Debiasing for Byzantine-Tolerant Peer Sampling