TARE: Weigh a Never-Poisoned Twin Before Reading Backdoor-Defense Costs
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Abstract (arXiv)
Backdoor-defense leaderboards print a clean-accuracy drop and read it as removal cost. Measured on the poisoned victim alone, the drop cannot separate removal from what the defense does to any model, and inherits the victim's start, which for three of BackdoorBench's sixteen attacks is a configuration file: WaNet, BPP and Input-Aware ship a MultiStepLR that never fires, so their victims never anneal and are the least accurate in 30/31 public CIFAR cells at ≤5%. On PreAct-ResNet18, fine-tuning-family defenses return a low start to their own level, so there the published cost is negative, the benchmark's rating clips the "gain" to zero, and 2 of 48 citing defense papers we read rest a no-cost claim on those cells; TSBD and CGD, re-run with their code, "gain" on a never-poisoned model too. A 2×2 editing only that scheduler line isolates the cause, its swapped arms self-registered before they ran: the sign of the fine-tuning family's clean-model cost reverses both ways while its published gain on the annealed victim only shrinks toward zero, 44/44 seeds following the schedule, replicated on BPP, FT-SAM, CIFAR-100 and VGG19-BN and induced in a second toolkit. TARE runs the same defense on a never-poisoned twin of the same recipe, schedule and seed (on BackdoorBench, ≤10 poisoned images, admitted only below 5% attack success); what the twin loses is the tare. On the BadNets grid seven of eight defenses charge the twin (Neural Cleanse only where its detector fires), +0.13 (fine-tuning) to +5.70 points (I-BAU); the eighth, ABL, destroys it. Within an attack the start cancels from rankings, so the tare re-orders nothing there; what poisoning adds beyond it is printed under two estimators and not corrected, its removal share unidentified. We ship the three-key patch, a signed tare column (7 attacks × 8 defenses) and TARE-Z, a twin-free estimator for seed-stable defenses.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 55 of its 72 references that are in the map. First posted on arXiv 4 Oct 2026.
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55 of 72 references.
References · 55
- 2022Editing Models with Task Arithmeticcited by 1,120
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- 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,055
- 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 580
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- 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 185
38 more
- 2024Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearningcited by 172
- 2021Adversarial Unlearning of Backdoors via Implicit Hypergradientcited by 166
- 2021Rethinking the Backdoor Attacks' Triggers: A Frequency Perspectivecited by 161
- 2020Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustnesscited by 132
- 2025The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injectionscited by 129
- 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 119
- 2024How Do Large Language Models Acquire Factual Knowledge During Pretraining?cited by 90
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- 2025The Leaderboard Illusioncited by 66
- 2023Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimizationcited by 64
- 2023Reconstructive Neuron Pruning for Backdoor Defensecited by 59
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- 2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Featurescited by 37
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- 2026Benchmark^2: Systematic Evaluation of LLM Benchmarkscited by 13
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- 2025Mapping Post-Training Forgetting in Language Models at Scalecited by 11
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- 2026Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Referencecited by 7
- 2024Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activenesscited by 6
- 2026(How) Learning Rates Regulate Catastrophic Overtrainingcited by 6
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- 2025A Single Character can Make or Break Your LLM Evalscited by 5
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- 2025SoK: The Last Line of Defense: On Backdoor Defense Evaluationcited by 5
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- 2026PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMscited by 3
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- 2026Quantifying the Effect of Test Set Contamination on Generative Evaluationscited by 2
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- 2022Fine-Tuning Is All You Need to Mitigate Backdoor Attacks
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