Long T. Le — accepted AI-conference papers

2026

CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution

Minbeom Kim, Mihir Parmar, Phillip Wallis, Lesly Miculicich, Kyomin Jung, Krishnamurthy Dvijotham +2

ICML 2026poster

AI agents equipped with tool-calling capabilities are susceptible to *Indirect Prompt Injection* (IPI) attacks. In this attack scenario, malicious commands hidden within *untrusted* content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often su…

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2026

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Pengfei He, Ash Fox, Lesly Miculicich, Stefan Friedli, Daniel Fabian, Burak Gokturk +4

ICML 2026poster

Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Co-RedTeam, a security-aware mul…

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2026

TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems

Md Atik Ahamed, Mihir Parmar, Palash Goyal, Yiwen Song, Long T. Le, Qiang (Shaun) Cheng +4

ICML 2026poster

We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely on numerical accuracy, treating foundation models as "black boxes." Unlike existing benchmarks, TFRBench provides a prot…

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