How should security teams reduce DLL hijacking risk when legacy desktop apps gain AI plugins?

Why This Matters for Security Teams

DLL hijacking risk rises sharply when legacy desktop applications start loading AI plugins because the plugin becomes part of the app’s trusted execution path. That changes the threat model from “feature add-on” to “code execution near sensitive workflows.” If the application searches writable directories, accepts relative paths, or loads libraries without verification, an attacker only needs one placement opportunity to substitute a malicious DLL.

This is not just a Windows hygiene issue. It is an application trust-boundary problem that maps directly to the broader supply chain and runtime integrity concerns described in the OWASP NHI Top 10 and the NIST Cybersecurity Framework 2.0. Security teams often miss the fact that a plugin can inherit the app’s privileges, access tokens, and file-system reach even when the plugin itself looks benign.

NHI Management Group research on JetBrains Marketplace AI Plugin Campaign shows how quickly plugin trust can be abused once the extension ecosystem becomes part of daily operations. In practice, many security teams encounter DLL hijacking only after a plugin rollout has already widened the application’s attack surface.

How It Works in Practice

The strongest control is to make library loading deterministic. That means using full paths for every DLL load, removing current-directory search behavior, and preventing the application from resolving libraries from user-writable locations. Where the vendor supports it, application manifests, safe DLL search mode, and explicit allowlists should be used together rather than as isolated fixes.

Runtime validation matters because packaging controls alone do not prove what is actually loaded in memory. Teams should monitor loaded modules, compare hashes or signatures for high-risk libraries, and alert on unexpected loads from temp folders, profile directories, or plugin cache paths. This is especially important when an AI plugin introduces its own dependency chain and can silently pull in additional libraries.

Current guidance from NIST SP 800-53 Rev 5 Security and Privacy Controls supports software integrity, least privilege, and configuration control, but the operational translation for DLL hijacking is straightforward:

• Force absolute paths for approved DLLs.
• Remove write access from folders in the search order.
• Require code signing or publisher trust for plugin-delivered components.
• Instrument endpoint detection to flag unexpected module loads.
• Test plugin updates in an isolated environment before broad rollout.

AI plugins also deserve separate review because they may call local tools, child processes, or model clients that introduce new load paths. The Ultimate Guide to NHIs -- Key Challenges and Risks is useful here because it frames plugin trust as part of the broader non-human execution chain, not just code distribution. These controls tend to break down when legacy apps rely on hardcoded relative paths or when plugin vendors ship frequent unsigned updates into heavily customized desktop environments.

Common Variations and Edge Cases

Tighter DLL controls often increase deployment friction, requiring organisations to balance attack reduction against compatibility issues, plugin breakage, and vendor support limits. The hardest cases are line-of-business desktop apps that use old frameworks, hardcoded search paths, or side-by-side dependency folders that cannot be reworked quickly.

There is no universal standard for this yet, but current guidance suggests prioritising the highest-risk combinations first: writable installation directories, plugins that handle secrets or authentication, and apps used by privileged operators. In some environments, endpoint block rules can reduce exposure faster than code changes, especially when the vendor cannot patch quickly.

One practical shortcut is to treat AI plugins as privileged software supply chain components rather than casual extensions. That means security teams should review the plugin’s load behavior, not only its declared permissions, and correlate suspicious module loading with plugin install events and update events. The Top 10 NHI Issues and the Ultimate Guide to NHIs -- Why NHI Security Matters Now both reinforce the same operational reality: once machine-driven components are trusted inside a desktop workflow, the blast radius can grow faster than traditional app hardening assumes.

Related resources from NHI Mgmt Group

• How should security teams reduce the risk of AI desktop apps turning account compromise into code execution?
• How should security teams reduce risk from AI agents and developer tools that use secrets locally?
• How should security teams reduce the risk of AI tool poisoning?
• How should security teams reduce indirect prompt injection risk in AI systems?