21 : 08 : 10 : 43

21 : 08 : 10 : 43

21 : 08 : 10 : 43

21 : 08 : 10 : 43

Keep Apart Research Going: Donate Today

Jun 30, 2024

Gradient-Based Deceptive Trigger Discovery

Henning Bartsch, Leon Eshuijs

Details

Details

Arrow
Arrow
Arrow
Arrow
Arrow
Arrow

To detect deceptive behavior in autoregressive transformers we ask the question: what variation of the input would lead to deceptive behavior? To this end, we propose to leverage the research direction of prompt optimization and use a gradient-based search method GCG to find which specific trigger words would cause deceptive output.

We describe how our method works to discover deception triggers in template-based datasets. Therefore we test it on a reversed knowledge retrieval task, to obtain which token would cause the model to answer a factual token. Although coding constraints prevented us from testing our setup on real deceptively trained models we further describe this setup

Cite this work:

@misc {

title={

Gradient-Based Deceptive Trigger Discovery

},

author={

Henning Bartsch, Leon Eshuijs

},

date={

6/30/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Apr 14, 2025

Read More

Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Read More

Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.