Frida is perhaps the most-used hooking framework by researchers, reverse engineers, and, indeed, attackers. Hooking frameworks enable researchers to "intercept and modify the behavior of mobile applications at runtime".
In our just-released App Threat Report, Promon's research team explored how attackers modify Frida to evade detection. We also explored how 150 of the most-used Android apps (with hundreds of millions of daily active users) handled hooking attempts by an unmodified Frida version.
A constant cat-and-mouse game
The detection-evasion struggle is a classic cat-and-mouse scenario. As defenders introduce new checks—scanning memory, enumerating libraries, tracking suspicious threads—attackers respond with obfuscation, custom hooks, and runtime manipulation. This interplay constantly shifts, requiring defenders to stay vigilant and adapt quickly.
Complicating matters further is the variability among devices and OS versions. A technique effective on one device may produce false positives or miss Frida entirely on another. Meanwhile, deep inspection methods can hurt app performance or degrade user experience.
Real-world analysis: the top 150 Android apps
Promon researchers examined 150 of the world’s most popular Android apps by monthly active users. These well-known apps collectively serve hundreds of millions of users, but how effectively do they detect a standard, unmodified Frida attempt?
The findings were stark. Of 144 successfully tested apps, only 3 responded appropriately to Frida’s presence. Another 2 apps showed ambiguous behavior, suggesting possible detection but not conclusively. In other words, just over 2% of these top-tier apps actively identified Frida out of the box.
For attackers, this is great news: they can exploit most high-profile apps with minimal resistance. For defenders and developers, it’s a wake-up call. Despite widespread awareness of Frida, few apps implement even basic detection measures.
The road ahead: machine learning, hardware assistance, and ethics
As the battle intensifies, detection strategies will likely evolve:
Machine Learning-Based Detection
Instead of relying on static signatures, defenders may use behavioral analysis models that learn what “normal” looks like and flag deviations that suggest Frida’s presence.Hardware-Assisted Techniques
Hardware-backed integrity checks can make tampering more difficult. Features like secure enclaves or trusted execution environments could limit what Frida can achieve.
Constantly Evolving Evasion
Attackers will never stop innovating. As defenders integrate advanced detection methods, attackers will refine their stealth techniques.
Ethical considerations also loom large. While Frida is a powerful and legitimate tool for security research, it can just as easily enable malicious activities. Security professionals must use these tools responsibly, only after proper authorization, and disclose vulnerabilities to affected parties to improve overall app security.
Strengthening your defense
Our findings show a glaring gap in current defenses. With only a handful of apps detecting an unmodified Frida agent, the mobile ecosystem is largely open to runtime manipulation. Developers should view this as an urgent call to action. Incorporating Frida detection mechanisms can significantly improve an app’s resilience against tampering and data theft.
While there’s no silver bullet, a layered approach—combining known detection methods with more advanced, behavior-driven techniques—can shift the balance. By proactively adopting robust defenses, developers and security teams can better protect user data, maintain application integrity, and help ensure that the mobile ecosystem evolves toward a more secure future.
Ready to dive in? Read the full report for in-depth findings and actionable guidance.
