4 articles in this topic.
This topic page curates research-focused writing on Binary Analysis, with an emphasis on practical security implications, reproducible observations, and implementation-aware takeaways. Instead of isolated summaries, the collection is organized to help you connect attack techniques, defensive controls, and evaluation criteria across multiple papers and project write-ups.
Across 4 articles, this cluster highlights how Binary Analysis appears in real workflows and where teams commonly miss risk boundaries. The coverage includes news digest, research paper, paper review and connects this theme with adjacent areas such as LLM Security, Adversarial ML, Data Poisoning, so you can move from conceptual understanding to deployable engineering decisions.
This page is maintained as a high-signal index for Binary Analysis. Use it to follow newer articles first, then branch into adjacent topics and defensive patterns that repeatedly appear across projects and paper reviews.
This digest covers advanced LLM security threats, including dynamic inference-time exploits, structural prompt injection, and loader-level defenses against shared object hijacking.
The dominant theme in today's landscape is the operational shift toward real-time, inference-stage intervention over destructive weight-modification, manifesting in both AI safety steering and highly
We introduce asmFooler, a framework that reveals deep learning-based binary code similarity detection models are highly vulnerable to adversarial semantics-preserving transformations at the binary level.
An analysis of Idioms, a framework that advances neural decompilation by jointly predicting source code and user-defined type definitions, using neighboring function context from call graphs to achieve up to 205% improvement over baselines on realistic code.