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  3. The Trigger in the Haystack: Extracting and Reconstructing L
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The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

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Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

PDF: https://arxiv.org/pdf/2602.03085v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Paper Mode

The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

Overall score: 2/10
Lineage: 4f65b1c44f8a…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
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Prior Work
Removing the Trigger, Not the Backdoor: Alternative Triggers and Latent Backdoors
Score 2.0stable
Higher Viability
BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents
Score 7.0up
Higher Viability
Detecting and Eliminating Neural Network Backdoors Through Active Paths with Application to Intrusion Detection
Score 3.0up
Higher Viability
Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
Score 7.0up
Higher Viability
Test-Time Attention Purification for Backdoored Large Vision Language Models
Score 7.0up
Higher Viability
Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
Score 7.0up
Higher Viability
Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
Score 5.0up
Higher Viability
Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning
Score 7.0up

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