TrojFSP: Trojan Insertion in Few-shot Prompt Tuning

Alapfogalmak

TrojFSP introduces a method to address security challenges in few-shot prompt tuning by balancing attack success rate and clean data accuracy.

Kivonat

• Abstract:
  • Prompt tuning is effective for adapting pre-trained language models (PLMs) with few samples.
  • TrojFSP addresses security issues like poisoned imbalance and overfitting in few-shot prompt tuning.
• Introduction:
  • Prompt-tuning adapts PLMs to new tasks, motivating prompt-based Trojan attacks.
  • Existing backdoors are not suitable for few-shot prompt-tuning due to high training costs.
• Data Poisoning Challenges:
  • Poisoned imbalance issue leads to low clean data accuracy in non-target classes.
  • Overfitting is common when generating backdoored prompts via few-shot tuning.
• TrojFSP Methodology:
  • TC-Shrink balances the number of poisoning samples across classes.
  • Selective Token Poisoning targets specific tokens to boost attack performance.
  • Trojan-Trigger Attention amplifies attention on poisoned trojan prompts.
• Experimental Results:
  • TrojFSP achieves high ASR while maintaining CDA across various PLMs and datasets.

Statisztikák

Transferring established data poisoning attacks directly to few-shot prompt tuning presents multiple challenges.
Experiments show that TrojFSP achieves an ASR of over 99% while maintaining negligible decreases in CDA across various PLMs and datasets.

Idézetek

"Prompt tuning is one of the most effective solutions for adapting pre-trained language models."
"TrojFSP improves the ASR by 9% ∼ 48% and the CDA by 4% ∼ 9% across various PLMs and downstream tasks."

Főbb Kivonatok

TrojFSP

by Mengxin Zhen... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2312.10467.pdf

Mélyebb kérdések

How can TrojFSP be adapted to defend against unseen triggers?

TrojFSP can be adapted to defend against unseen triggers by implementing a proactive defense mechanism that focuses on identifying and neutralizing potential backdoors before they are activated. One approach could involve developing robust detection algorithms that analyze the prompt structure and token interactions to identify any suspicious patterns indicative of a backdoor. By incorporating anomaly detection techniques, the system can flag prompts with unusual characteristics for further investigation.
Additionally, introducing dynamic prompt validation during inference can help mitigate the risk posed by unseen triggers. This involves continuously monitoring model outputs for unexpected behavior when processing input samples and triggering an alert if any deviations from normal operation are detected. By leveraging real-time monitoring and response systems, TrojFSP can enhance its resilience against unforeseen trigger-based attacks.

How ethical considerations should be taken into account when researching backdoor attacks?

When conducting research on backdoor attacks in NLP systems, several ethical considerations must be carefully addressed:

Transparency: Researchers should maintain transparency about their intentions and clearly communicate the purpose of studying backdoor attacks in NLP systems.

Informed Consent: If human subjects are involved in experiments or studies related to backdoor attacks, obtaining informed consent is crucial to ensure participants understand the risks involved.

Data Privacy: Safeguarding sensitive data used in experiments is essential to protect individuals' privacy rights and prevent unauthorized access or misuse of personal information.

Mitigating Harm: Researchers should take measures to minimize potential harm caused by their research findings, such as developing effective defense mechanisms or responsible disclosure practices for vulnerabilities discovered.

Accountability: Maintaining accountability for the implications of research on backdoor attacks is vital, including being prepared to address any unintended consequences that may arise from the study's outcomes.

By adhering to these ethical principles, researchers can conduct responsible investigations into backdoor attacks while prioritizing data security, participant well-being, and societal trust in NLP technologies.

How can the findings of this research be applied to other NLP tasks beyond classification?

The insights gained from this research on TrojFSP's effectiveness in few-shot prompt-tuning for classification tasks have broader applications across various NLP domains:

Text Generation: The strategies employed in TrojFSP, such as selective token poisoning and attention-aware optimization, can be leveraged in text generation tasks like machine translation or dialogue generation to enhance model robustness against adversarial inputs.

Named Entity Recognition (NER): Applying TC-Shrink methodology could improve few-shot tuning performance for entity recognition tasks by balancing class distributions within limited training samples.

Sentiment Analysis: Techniques introduced in TrojFSP like Trojan-Trigger Attention could benefit sentiment analysis models by enhancing their ability to capture subtle contextual cues related to sentiment polarity more effectively.

Summarization & Question Answering: Insights from this study could inform advancements in summarization models through improved prompt adaptation methods tailored towards generating concise summaries based on limited input examples.

By extrapolating key methodologies from TrojFSP across diverse NLP tasks beyond classification alone, researchers can advance model adaptability under constrained training scenarios while bolstering overall performance across multiple application areas within natural language processing realms