Abliterated Models in Appsec Pentesting

Our team examines abliterated models and what scenarios in which security professionals should consider using them. This post briefly explains abliteration as a concept, discusses how it might make exploit development easier, and also discusses the risks of the current abliteration model landscape.

EncryptedSharedPreferences is Dead: Here’s What You Should Use Instead

Android application developers often store sensitive data to disk, relying on physical device security and process isolation to prevent attackers from obtaining that data. This goes against security best practices, and doesn’t take advantage of the latest tools made available by Google to mitigate the risk of locally-stored sensitive data. This post explores what those tools are, discusses previous (now deprecated) methods for securing data locally, and provides recommendations for what Android developers should do if storing sensitive data is unavoidable.

Web App Pentesting in the AI Era

The IncludeSec team explores the practical considerations of AI-assisted source code analysis. Observing results produced with frontier vs locally-hosted models, various harness orchestration designs, and a list of different language frameworks provided us with meaningful insights into how the modern pentester should be using AI to produce higher quality results (but not necessarily in less time!)

CTFs in the AI Era

The Include Security team attended the BSides 2026 CTF competition and observed how effective frontier models have become at solving a variety of CTF challenges. Our team gives a first-hand account of how the winning strategy has recently shifted to prioritizing efficient solve times and orchestration pipelines that can combine the speed of lighter-weight models on easier challenges with a heavy-hitting model’s reasoning capabilities. We also highlight why the success of LLMs in CTFs does not translate as effectively in the arena of professional security assessments.