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Rabimba Karanjai

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#artificial intelligence Preprint Oct 2026

CM-DPO: Constraint-Margin Direct Preference Optimization for LLM Planning

Direct Preference Optimization (DPO) treats all constraint violations equally: a $1 budget overshoot and a $1,000 overshoot induce the same training signal. It is also susceptible to length and style bias when preference pairs come from different model families. We introduce Constraint-Margin DPO (CM-DPO), which replac...

Rabimba Karanjai, Qun-Hui Gu, Hemanth Hegadehalli Madhavarao et al. · 0 citations
Preprint Aug 2026

Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection

SkillGuard is presented, a harness-level enforcement layer that treats this event as contamination and restricts future capabilities to disconnect the resulting state from deployer-defined forbidden states and preserves substantially more capabilities than binary restriction at the same attack success rate.

Wu-Jie Xiong, Rabimba Karanjai, Yang Lu et al. · 2 citations
Preprint Aug 2026

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution, is presented and frame its claims as logic-level safety within a reproducible benchmark rather than deployment-ready DeFi security.

Rabimba Karanjai, Yang Lu, Richard Williamson et al. · 1 citation
Preprint Jul 2026

Context Contamination in LLM Analysis of Network Security Logs: Poison with Passive Prompt Injection and Mitigation Evaluation

The results establish that LLM-based log analysis creates an inherent confused deputy vulnerability where untrusted data and trusted instructions compete indistinguishably for model attention, requiring defense in-depth architectures and continued human oversight for security-critical decisions.

Rabimba Karanjai, Yang Lu, H. Madhavarao et al. · 2 citations

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