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DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation

Houcheng Jiang, Zetong Zhao, Junfeng Fang, Haokai Ma, Ruipeng Wang, Xiang Wang, Xiangnan He, Yang Deng · Jun 16, 2025 · Citations: 0

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How to use this paper page

Coverage: Stale

Use this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.

Best use

Background context only

Metadata: Stale

Trust level

Low

Signals: Stale

What still needs checking

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Signal confidence: 0.15

Abstract

Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying a small set of parameters to map triggers to attacker-desired behaviors. However, we find that existing editing-based attacks are often unstable under safety alignment: the edited model may start with an affirmative prefix but later revert to refusals during generation. We term this phenomenon safety fallback. To mitigate it, we propose DualEdit, a dual-objective model editing framework that simultaneously promotes affirmative tokens and suppresses refusal tokens. DualEdit further addresses two key challenges, objective imbalance and refusal diversity, via two complementary techniques: (1) dynamic loss weighting, which calibrates the relative scales of the two objectives using the pre-edited model to stabilize optimization, and (2) value anchoring, which clusters representative attention value vectors to form compact anchors, reducing conflicts from overly diverse token sets and improving generalization. Experiments on safety-aligned LLMs show that DualEdit improves attack success by 10% and reduces safety fallback rate by 11% over baselines.

Use caution before copying this protocol

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.15 (below strong-reference threshold).
  • No explicit evaluation mode was extracted from available metadata.
  • No benchmark/dataset or metric anchors were extracted.
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What This Page Found In The Paper

Each field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

missing

None explicit

Confidence: Low Not found

No explicit feedback protocol extracted.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Evaluation Modes

missing

None explicit

Confidence: Low Not found

Validate eval design from full paper text.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Quality Controls

missing

Not reported

Confidence: Low Not found

No explicit QC controls found.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Not found

No benchmark anchors detected.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Reported Metrics

missing

Not extracted

Confidence: Low Not found

No metric anchors detected.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Rater Population

missing

Unknown

Confidence: Low Not found

Rater source not explicitly reported.

Evidence snippet: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Signal basis: Structured extraction plus abstract evidence.

Evaluation Lens

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Signal confidence: 0.15
  • Known cautions: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.
  • Recent model editing-based approaches enable efficient backdoor injection by directly modifying a small set of parameters to map triggers to attacker-desired behaviors.
  • However, we find that existing editing-based attacks are often unstable under safety alignment: the edited model may start with an affirmative prefix but later revert to refusals during generation.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.
  • To mitigate it, we propose DualEdit, a dual-objective model editing framework that simultaneously promotes affirmative tokens and suppresses refusal tokens.
  • Experiments on safety-aligned LLMs show that DualEdit improves attack success by 10% and reduces safety fallback rate by 11% over baselines.

Why It Matters For Eval

  • Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks.
  • Experiments on safety-aligned LLMs show that DualEdit improves attack success by 10% and reduces safety fallback rate by 11% over baselines.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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