Injection Location Matters: Trust Region Effects in LLM Agents
Where You Inject Matters: The Trust Region Effect in LLM Agents
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Researchers from Microsoft Research and other institutions systematically measured the effect of injection location on attack effectiveness in indirect prompt injection, and propose a trust region effect, whereby agents implicitly assign different levels of trust to different regions of the environment. The team built three testbeds covering tool use, coding, and everyday workflows, containing 30 long-horizon benign tasks, 24 categories of injection risk, and 36 environmental injection surfaces. With injection content held fixed, the frequency with which GPT-5.4 follows injected instructions in repository documents such as README or TODO is on average 2.6 times that for code or data files; most injections that are delivered but fail are ignored rather than explicitly refused. Injections are more effective when located at the end of a surface; in the tool-use domain, the end is 11.7 percentage points higher than the beginning. The researchers also trained a two-stage attacker that jointly optimizes content and location, outperforming a content-only baseline with self-selected locations by 20.7 to 24.8 percentage points on unseen risk settings; training only against GPT-4o achieves a 33.3% attack success rate on GPT-5.4, whereas a template attack achieves 0%.
- LLM agents read external environment content during long-horizon tasks and are vulnerable to indirect prompt injection attacks. Existing research has mainly focused on the injected content, while the impact of injection location (environment surface and position within the surface) on attack effectiveness has not been systematically characterized.
- Build agentic testbeds covering three domains—coding, tool use and daily workflow—with a total of 36 injection surfaces and 24 risk categories; fix the injected content and vary only the location, measure conditional ASR, and propose a two-stage (content first, then location) GRPO-learned attacker.
- The same content shows markedly different effects across surfaces (e.g., GPT-5.4's compliance rate in README/TODO is about 2.6 times that in code or data files), which the authors call the trust region effect; injection at the end of a surface is usually more effective; failures are mostly ignored rather than refused; two-stage optimization outperforms content-only optimization when the policy chooses positions itself, and transfers to the stronger GPT-5.4.
- The trust region effect is only an empirical behavioral phenomenon and does not directly measure the agent's internal "trust"; it covers only 3 test environments and does not capture the full diversity of the real world; the study is limited to indirect prompt injection, and whether it applies to other agent safety risks remains to be studied.
The paper fixes the injected content and varies only its location across three testbeds to isolate the measurement, adding injection location—an often overlooked dimension—to the design of agent defenses.