New Framework VIGIL Aims to Balance Security and Utility in LLM Tool Streams
Global: New Framework VIGIL Aims to Balance Security and Utility in LLM Tool Streams
Researchers announced a new framework called VIGIL in a preprint posted to arXiv in January 2026, targeting large language model (LLM) agents that operate in open environments and are vulnerable to indirect prompt injection attacks.
Background on Prompt Injection
Indirect prompt injection occurs when manipulated metadata or runtime feedback within the tool stream of an LLM agent alters the execution flow, potentially causing the model to follow malicious instructions.
Limitations of Current Defenses
Existing defensive approaches face a trade‑off: dynamic defenses that preserve adaptive reasoning can be overridden by injected rules, while static isolation mechanisms cut off the feedback loop that LLMs need for effective problem solving.
The VIGIL Framework
VIGIL proposes a verify‑before‑commit protocol that allows the model to generate speculative hypotheses but requires intent‑grounded verification before any action is executed, thereby maintaining reasoning flexibility while enforcing safety.
Introducing the SIREN Benchmark
The authors also released SIREN, a benchmark comprising 959 tool‑stream injection cases designed to emulate dynamic dependencies and pervasive threats across a range of scenarios.
Experimental Findings
According to the preprint, VIGIL reduced the attack success rate by more than 22 % compared with state‑of‑the‑art dynamic defenses and more than doubled utility under attack relative to static baselines.
Implications and Future Directions
The results suggest that verification‑centric designs may offer a viable path toward reconciling security and utility in LLM agents, and the authors indicate plans to extend the framework to additional tool integrations.
This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.
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