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AI security threat model:
A comprehensive approach
From threat model to mitigation plan: How to secure AI inside mobile apps
AI is now inside the app—not just behind it. As organizations embed AI directly into mobile experiences, the number and complexity of threats have exploded.
This new paper by Dr. Anton Tkachenko introduces a complete AI security framework for identifying, categorizing, and prioritizing risks across device, model, application, and agent levels. It also shows how Promon’s protection layers map to these threats to defend against real-world AI attacks.
What you'll learn
- The 49 key AI threats targeting on-device and embedded models
- How to classify and prioritize AI risks across your app ecosystem
- Practical defense strategies based on OWASP, MITRE, and NIST frameworks
- How Promon’s Shield, Data Protect, and Code Protect mitigate the most critical AI threats
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Watching the apps we protect: How we found Hirudo by accident
A side project to track protected apps led our researchers to Hirudo malware and a leaked Android attestation keybox. We reported the leak to Google and recreated the attack to test whether Promon Shield could withstand it.2 Nov 2026
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The missing signal in gaming fraud: What the running game can tell you
Gaming fraud systems can see what players do, but not always what is happening inside the game client. In-app runtime signals add that missing context, helping teams connect manipulation with wider player, account, and device behavior, to ensure successful launches, fair play, and community respect.27 Oct 2026
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How agentic attacks could change mobile app security
In the publicly documented malicious campaigns reviewed for this post, we found no evidence of an AI agent running the full attack chain against a mobile application. But AI is already taking over individual stages, changing the economics of reverse engineering, runtime instrumentation, and attack adaptation.20 Oct 2026
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Don’t make it easy for them: What Cybersecurity Awareness Month means at runtime
Cybersecurity Awareness Month 2026 is about making life harder for attackers. At runtime, that means raising the cost of analyzing, tampering, and abusing web and mobile applications.12 Oct 2026
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Beyond automation: When attackers become agentic
AI is beginning to take over the decisions between automated attack stages, not just execute the steps themselves. As systems learn from results, pivot approaches, and coordinate with other agents, the attacker starts to behave more like a feedback loop.8 Oct 2026
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How responsible disclosure helped us strengthen Shield for Mobile on Android
Independent security researcher 0xobfuscated developed a protection-bypass technique against Promon Shield for Mobile™ on Android. They contacted us before publishing, giving our teams time to investigate the research, strengthen the protection, and make updated releases available to customers.5 Oct 2026
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Learn globally, act locally: Why app security needs a runtime intelligence loop
Attacks no longer stay inside one app, one region, or one stage of the kill chain. A runtime intelligence loop helps app protection learn from the wider threat picture. Then it turns that learning into detectors, policies, and controls that run locally inside protected apps.28 Sep 2026
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App Threat Report Q3 2026: GPU debugger abuse in Android apps
GPU debugging tools were built to help developers inspect rendering. But the same mechanisms can become an injection path when turned against a shipping app. Our research shows how Vulkan layers can be abused on Android, with mobile games as a particularly attractive target.23 Sep 2026
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