Adam Conway writes that Qwen 3.8 27B, a 17 GB open-weights model running on a single Lenovo ThinkStation PGX, completed a commercial app's license reverse-engineering task in roughly 30 minutes'', statically disassembling arm64 code, recovering a deliberately obscured RSA public key, self-correcting an initial hash mismatch, and producing a working authentication bypass entirely offline.
- The model detected the jailbreak prompt, correctly identified the real developer from the signing certificate, then proceeded to audit the scheme and ultimately built the bypass on its own
- The target app's licensing was described as unusually thorough for its class: one-time online activation, offline signature check, hardware serial binding, embedded revocation list, and signed update path
- The model ran via the Pi harness using only standard Bash-based tools; no execution of the target app occurred until the final bypass demonstration
- Default max reasoning effort means even trivial requests consume hundreds to thousands of tokens, making the self-correction loop verbose but accurate
- Conway frames this as a shift in the threat model: a capable local model with no cloud oversight gives full control to whoever is at the keyboard
- The model detected the jailbreak prompt, correctly identified the real developer from the signing certificate, then proceeded to audit the scheme and ultimately built the bypass on its own
- The target app's licensing was described as unusually thorough for its class: one-time online activation, offline signature check, hardware serial binding, embedded revocation list, and signed update path
- The model ran via the Pi harness using only standard Bash-based tools; no execution of the target app occurred until the final bypass demonstration
- Default max reasoning effort means even trivial requests consume hundreds to thousands of tokens, making the self-correction loop verbose but accurate
- Conway frames this as a shift in the threat model: a capable local model with no cloud oversight gives full control to whoever is at the keyboard
Emmimal P Alexander writes that while prompt engineering focuses on optimizing LLM inputs, managing these templates within evolving codebases often leads to production crashes when variables are renamed or removed. To solve this, she created `promptctl`, a Python tool that applies static analysis—similar to database schema migrations—to ensure prompt variable contracts match their call sites in the codebase.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
- Performs PromptDiff (detects changes), Contract Validation (checks mismatches), and Impact Analysis (traces dependencies).
- Operates strictly via AST parsing, requiring zero LLM calls or API keys.
- Detects errors that unit tests often miss by mocking away the actual string formatting step.
High-level diagram representations for code. CodeBoarding is an open-source codebase analysis tool that generates high-level diagram representations of codebases using static analysis and LLM agents, that humans and agents can interact with.
