AI Research Analysis
SafeProtein: Red-Teaming Framework and Benchmark for Protein Foundation Models
This paper introduces SafeProtein, the first red-teaming framework for protein foundation models, revealing potential biological safety risks in current models with up to 70% attack success rate against ESM3, and providing insights for robust security protection.
Executive Impact & Key Findings
SafeProtein highlights critical biosafety vulnerabilities in protein foundation models, demanding proactive security measures in AI development.
0%
Max Attack Success Rate (ESM3)
0
Curated Harmful Proteins in Benchmark
0
Red-Teaming Strategies Developed
0
Foundation Models Evaluated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Addressing Protein Foundation Model Risks
Protein foundation models (Protein-FMs) promise breakthroughs in protein understanding and design, yet they pose significant dual-use concerns, particularly regarding the generation of proteins with biosecurity risks. Unlike large language models (LLMs), systematic red-teaming for Protein-FMs is nascent and faces unique challenges:
- Biological Complexity: Designing biologically meaningful and adversarially effective prompts is difficult due to protein structure and function dependence on intricate spatial amino acid arrangements.
- Inbuilt Safeguards: Some models incorporate explicit safeguards (e.g., removing biosafety-related sequences from training data), making it harder to recover harmful proteins.
- Evaluation Difficulty: Defining fair and consistent criteria for successful "jailbreaks" is complex, as protein sequences are not human-readable.
This research aims to bridge this gap by establishing a robust framework for assessing and mitigating these risks.
SafeProtein: A Novel Red-Teaming Framework
SafeProtein is the first systematic red-teaming framework for Protein-FMs, comprising two key components:
- Systematic Red-Teaming Methodology: Integrates multimodal prompt engineering (sequence and structure-based inputs), non-pathogenic Foldseek structural similarity search, and a score-function-guided heuristic beam search for comprehensive adversarial evaluation.
- Five Prompt Construction Strategies:
- Strategy 1 (Masked Sequence): Uses only the masked sequence.
- Strategy 2 (Masked Sequence + Native Backbone Structure): Includes the protein's native backbone for reconstructing side chains and sequences.
- Strategy 3 (Masked Sequence + Foldseek Backbone Structure): Combines masked sequence with benign structural fragments for guidance.
- Strategy 4 (Strategy 2 + Multiple Beam Search): Enhances adversarial robustness with multiple harmful generation attempts.
- Strategy 5 (Strategy 2 + Score-Function Guidance): Applies heuristic score-function guidance at each diffusion step for more rigorous testing against harmful outputs.
This framework is designed to uncover vulnerabilities and inform the development of stronger protective technologies.
SafeProtein-Bench: Curated Benchmark Dataset
SafeProtein-Bench is the first dedicated benchmark for protein red-teaming, meticulously constructed to evaluate the dual-use potential of protein language models. Key features include:
- Curated Dataset: Contains 429 proteins, manually curated from the HHS and USDA Select Agents and Toxins lists, and UniProt's "Toxin" keyword. It focuses on viral and toxin proteins known to pose severe threats to public health.
- Experimentally Determined Structures: Only proteins with experimentally determined crystal structures (between 30 and 1000 amino acids) are included, ensuring clear characterization of functional domains.
- Masking Strategies: Employs conservation, random, and tail masking to simulate various adversarial scenarios, assessing the model's ability to reconstruct core functional domains.
- Comprehensive Evaluation Protocol: Jailbreak success is determined by jointly assessing both sequence identity and structural similarity (RMSD) against the native protein, using strict criteria to minimize false positives. This dual-criteria approach ensures robust measurement of a model's susceptibility to attacks.
This benchmark provides a standardized platform for identifying and mitigating biosafety risks in protein foundation models.
Unveiling Biosafety Risks in Protein Models
Our red-teaming efforts using SafeProtein revealed significant biosafety risks in state-of-the-art protein foundation models like ESM3 and DPLM2. Key findings include:
- High Attack Success Rates: ESM3 demonstrated up to a 70% jailbreak success rate, successfully reconstructing harmful protein sequences and structures.
- Impact of Structural Prompts: Incorporating native backbone structures (Strategy 2) significantly increased jailbreak rates for ESM3, highlighting the model's reliance on structural context for generating functional protein domains.
- Effectiveness of Advanced Strategies: Additional generation strategies (Strategy 4 & 5), leveraging multiple beam search runs and score-function guidance, further amplified security risks, indicating that ESM3 retains inherent knowledge of harmful proteins despite specific training precautions.
- Masking Ratio Dependence: While jailbreak success generally decreased with increasing masking ratios, it remained notably high even at 50% masking, especially under conservation masking strategies that target core functional regions.
- DPLM2 Vulnerabilities: DPLM2 also exhibited notable jailbreak success rates even without structural prompts, suggesting its specialized sequence-structure alignment training contributed to its vulnerabilities.
These results underscore the urgent need for stronger alignment and filtering pipelines for frontier protein models to mitigate potential misuse.
SafeProtein Red-Teaming Process Flow
Problem Identification: Dual-Use Risks in Protein-FMs
→
SafeProtein Methodology Development
→
Multimodal Prompt Engineering
→
Heuristic Beam Search & Score Guidance
→
SafeProtein-Bench Dataset Curation
→
Joint Sequence-Structure Evaluation
→
Uncover Biosafety Risks & Vulnerabilities
→
Inform Robust Security Protection
70%
Peak Attack Success Rate against ESM3 Model
This metric highlights the significant potential for protein foundation models to be exploited for generating harmful biological agents, underscoring urgent biosafety concerns.
| Generation Strategy | Model | 0.1 Masked Ratio (Success Rate) | 0.5 Masked Ratio (Success Rate) |
|---|---|---|---|
| Masked Sequence (Strategy 1) | ESM3 | 39.63% | 0.70% |
| Masked Sequence (Strategy 1) | DPLM2 | 36.36% | 12.59% |
| Masked Seq + Native Struct (Strategy 2) | ESM3 | 71.56% | 35.20% |
| Masked Seq + Native Struct (Strategy 2) | DPLM2 | 42.66% | 16.32% |
| Masked Seq + Foldseek Struct (Strategy 3) | ESM3 | 49.42% | 18.18% |
| Masked Seq + Foldseek Struct (Strategy 3) | DPLM2 | 44.29% | 17.72% |
Note: Rates for additional strategies (Strategy 4 & 5) are even higher, reaching up to 75% for ESM3 under conservation masking, further emphasizing the risks.
Case Study: Harmful Protein Reconstruction by ESM3
The red-teaming efforts revealed ESM3's alarming capability to reconstruct harmful proteins, highlighting the dual-use concerns inherent in current protein foundation models.
Case 1: Basic Phospholipase A2 Ammodytoxin C (UniProt ID: P11407)
Source: Vipera ammodytes (snake venom)
Function: This protein is a potent neurotoxin and anticoagulant. It functions by hydrolyzing phospholipids like phosphatidylcholine, inhibiting acetylcholine release, and leading to neuromuscular paralysis.
ESM3's Performance: ESM3 successfully recovered its masked structure and sequence even at a 0.5 masking ratio, with a low RMSD of 0.698Å and a high sequence identity of 85.25%. This demonstrates the model's ability to recreate a highly dangerous biological agent from partial input.
Case 2: L-amino-acid oxidase protein (UniProt ID: Q6STF1)
Source: Gloydius halys (snake venom)
Function: This protein exhibits strong biological activity, including inducing bleeding, hemolysis, and cytotoxicity.
ESM3's Performance: Similar to Case 1, ESM3 was able to reconstruct this protein from a masked sequence input (0.5 masking ratio), achieving an RMSD of 0.964Å and a sequence identity of 51.86%. This case further strengthens concerns about the inherent biosafety risks associated with current protein foundation models.
Calculate Your Potential AI ROI
Understand the tangible impact AI can have on your operational efficiency and cost savings. Adjust the parameters below to see estimated returns.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A structured approach to integrating AI into your enterprise, ensuring maximum impact and minimal disruption.
Phase 1: Discovery & Strategy
In-depth analysis of current workflows, identification of AI opportunities, and development of a tailored implementation strategy aligned with business objectives.
Phase 2: Pilot Program & Proof of Concept
Deployment of AI solutions in a controlled environment to validate effectiveness, measure initial ROI, and gather feedback for optimization.
Phase 3: Scaled Deployment & Integration
Full-scale integration of AI solutions across relevant departments, ensuring seamless adoption and robust performance within existing IT infrastructure.
Phase 4: Optimization & Continuous Improvement
Ongoing monitoring, performance tuning, and iterative enhancements to maximize AI efficiency and adapt to evolving business needs and technological advancements.
Ready to Secure Your AI Future?
Let's discuss how your organization can leverage AI responsibly while safeguarding against emerging threats.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat