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Home/Authors/Mohammadreza Rashidi

Mohammadreza Rashidi

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

Crypto×3AI×2ML×2Vision×1

Frequent co-authors

Raja Hashim Ali1×
Sami Ur Rahman1×

Research Timeline

2026
Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks

This paper proposes a 3D CNN detector that leverages temporal artifacts to accurately identify high-quality deepfake videos, demonstrating robust detection even after social media re-encoding.

Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity

The paper investigates indirect prompt injection vulnerabilities in ReAct agents by systematically varying the injection depth, payload framing, and turn budget, finding that injection depth is the dominant risk factor.

Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity

This paper investigates indirect prompt injection vulnerabilities in ReAct agents by systematically analyzing how the injection depth and payload framing affect attack success rates, finding that injection depth is the dominant risk factor.

Highlighted terms show continued research focus across papers

Papers

cs.CRcs.AIcs.LGRecentMay 29, 2026

Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity

Mohammadreza Rashidi

The paper investigates indirect prompt injection vulnerabilities in ReAct agents by systematically varying the injection depth, payload framing, and turn budget, finding that injection depth is the do…

View →
cs.CRcs.AIcs.LGRecentMay 29, 2026

Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity

Mohammadreza Rashidi

This paper investigates indirect prompt injection vulnerabilities in ReAct agents by systematically analyzing how the injection depth and payload framing affect attack success rates, finding that inje…

View →
cs.CVcs.CRRecentMay 17, 2026

Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks

Mohammadreza Rashidi, Raja Hashim Ali, Sami Ur Rahman

This paper proposes a 3D CNN detector that leverages temporal artifacts to accurately identify high-quality deepfake videos, demonstrating robust detection even after social media re-encoding.

View →