Bacterial Biothreat Schema

• Bacterial biothreat schema is a formal framework that defines and quantifies risks from bacterial agents using structured taxonomies and measurable attributes.
• It enables systematic evaluation of both technical pathogenicity and operational risk with integrated risk scoring formulas and cyber-bio interfaces.
• Its applications include risk quantification, red-teaming simulations, dataset annotation, and guiding effective mitigation strategies in biosecurity.

A bacterial biothreat schema is a formal framework for representing, assessing, and benchmarking the technical and operational risk landscape posed by bacterial agents in biosecurity-relevant scenarios. This schema is essential for risk quantification, red-teaming, model evaluation, dataset annotation, and mitigation tooling in both traditional and AI-enabled contexts. It encompasses entities (e.g., threat agents, delivery vectors, actor profiles), taxonomies, relationships, quantitative metrics, and risk-scoring formulas, providing a multidimensional structure that supports systematic evaluation of both technical pathogenicity and situational adversarial risk.

1. Entity Types, Attributes, and Formal Taxonomies

Bacterial biothreat schemas codify the comprehensive structural elements associated with biocybersecurity threat scenarios (Potter et al., 2020). Entities are rigorously defined with attributes relevant for downstream analysis and machine-readable annotation:

• PathogenPayload: Identified by taxonomy (Genus/Species/Strain), genome size, virulence factors, LD50, host range, aerosol stability, growth rate, resistance genes, and stealth markers (e.g., CRISPR camouflage).
• AssociatedToxin: Characterized by toxin class (neurotoxin, enterotoxin, cytotoxin), molecular weight, potency, mode of action, and thermal stability.
• CyberEnabledSynthesisNode: Includes node type (biofoundry, DNA printer), network connectivity, automation level, vulnerability score, and access control.
• DeliveryVector: Specifies vector type (aerosol, fomite, water), stealth level, dispersal efficiency, and targeting mechanisms (e.g., geofencing, genetic marker).
• SupplyChainNode: Details product type, regulatory compliance, and device security posture.
• TelehealthDevice: Encodes device type, sensor modalities, firmware version, vulnerability score (CVSS).
• AttackerProfile: Captures capability tier (individual, hacktivist, state-sponsored), resourcing, and known targets.
• HostPopulation: Documents demographics, susceptibility profiles, and genetic marker frequencies.

Taxonomies are embedded according to threat-relevance:

• CDC-style categories (A/B/C), e.g., Bacillus anthracis, Yersinia pestis, Francisella tularensis.
• Toxin classes mapped to pathogenic payloads.
• Operational device and vector types mapped to cyber-bio integration points.

2. Hierarchical Architecture and Task-Query Mapping

The schema articulated in the BBG Framework is hierarchically structured with four principal levels—categories, elements, tasks, and query templates (Ackerman et al., 9 Dec 2025):

• Categories ($C$): Threat domains (e.g., Production, Delivery & Execution).
• Elements ($E(c_i)$): Sub-domains under each category (e.g., initial culturing, agent modification).
• Tasks ($T(e_{ij})$): Adversary-relevant activities aligned to each element.
• Query Templates ($Q(t_{ijk})$): Adversarial prompts operationalizing tasks.

The mapping proceeds: $c_i \to e_{ij} \to t_{ijk} \to q_{ijkl}$ This structure enables granular evaluation of biological threat scenarios, supporting alignment with empirical queries in benchmarking exercises.

3. Delivery Modalities and Cyber-Bio Integration

Delivery methods intersect biological, physical, and cyber infrastructure (Potter et al., 2020):

• Aerosol Drones: Remote dissemination via compromised autopilots, enabling geofenced release of high-priority agents (B. anthracis, Y. pestis).
• Fomite via SmartObjects: Infection vectors leveraging compromised IoT device firmware in ubiquitous items (e.g., smart toys, toothbrushes).
• Supply-Chain Compromise: Targeted distribution of contaminated goods through infiltrated supply chains informed by cyber-enabled user profiling.
• Telehealth Triggered Release: Time-locked infection events using compromised health devices.
• Information-Bio Fusion: Amplification of panic and degraded response via information warfare coupled to engineered agent release.

Cyber-bio interfaces are explicitly encoded, providing entry points for adversarial exploitation and defense modeling.

4. Risk Assessment Formulas, Quantitative Metrics, and Benchmarking

Multi-factor risk quantification is central to the schema paradigm:

$R = f(V, I, E)$

with

$R = \alpha\,V\,\times\,I\,\times\,E$

where

$V = \sum_{i=1}^N w_i v_i$

$$I = \beta_1\,\mathrm{VF} + \beta_2\,\mathrm{AeroStab} + \beta_3\,\mathrm{Resist}$$

$$E = \gamma_1\,\mathrm{PopDens} + \gamma_2\,\mathrm{Comorbid} + \gamma_3\,\mathrm{Seasonality}$$

Derived scores include supply-chain pivot risk $R_{\mathrm{SC}}$ and stealth synthesis detection score $D$, relevant for detection and audit processes.

Benchmark schemas such as B3 (Ackerman et al., 9 Dec 2025) employ composite SME-evaluated metrics:

• Technical: accuracy, completeness, novelty
• Operational: likelihood_of_acceptance, response_safety
• Composite scores: modified_risk_score, weighted_modified_risk_score (via acceptance penalty, novelty boost, refusal rate weighting)

Thresholds for letter grades are systematically determined, enabling quantifiable model and scenario risk tracking.

5. Schema Implementations: Data Structures, Relationships, and Model Integration

To support empirical evaluation, schemas are realized in both JSON-centric and SQL database architectures (Ackerman et al., 9 Dec 2025). Core entities map directly to structured fields:

Entity	Key Fields	Associated Quantitative Metrics
Benchmark	category, reasoning,	prompt_text, agent, location
Evaluation	response_text, refused	accuracy, completeness, novelty, likelihood_of_acceptance, response_safety
AggregatedMetrics	refusal_rate	risk scores, letter grades

Organism taxonomy extensions and regulatory annotations (e.g., biosafety level, select agent status) provide filtering and metadata enrichment capabilities. Relationships among entities strictly follow the schema specifications to maintain traceability and facilitate analytic queries.

6. Paradigm Scenarios and Threat Vectorizations

Potter et al. provide canonical threat paradigms enabling scenario-based red-teaming (Potter et al., 2020):

• Stealthy Synthesis (S1): Automated biofoundry bypass; low detectability, high composite risk.
• Remote Dissemination (S2): Wi-Fi-controlled drone swarms; targeted aerosol release.
• Supply-Chain Compromise (S3): Apparel factory infiltration; efficiency and regulatory deficits amplify pivot risk.
• Siege Warfare Information-Bio Fusion (S4): Sequential agent release and panic amplification.
• Surveillance Warfare (S5): Designer viruses encoding IR markers; real-time population tracking.

These paradigms instantiate the schema across multidomain threat surfaces, illustrating practical deployment for risk audit and blue-team/mitigation tool development workflows.

7. Actor Capabilities, Operational Risk, and Aggregated Scenario Scoring

BBG-based schemas integrate actor capability tiers ($\kappa_1$, $\kappa_2$, $\kappa_3$)—ranging from novice to expert adversaries—as explicit metadata (Ackerman et al., 9 Dec 2025). For operational risk, resource constraints, detection risk, and logistical complexity are quantified. The aggregated risk scoring for categories is formulated:

$$R(c_i) = \sum_{e_{ij} \in E(c_i)} \sum_{t_{ijk} \in T(e_{ij})} \sum_{\kappa \in \{\kappa_1, \kappa_2, \kappa_3\}} w_{ijk\kappa}\;f(d_{ijk},\,\kappa,\,r_{ijk})$$

This enables systematic simulation and evaluation of differential risk under varying adversary and operational profiles, optimizing mitigation and policy prioritization.

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Collectively, these schema frameworks represent the authoritative best practices for encoding, quantifying, and operationalizing bacterial biothreat scenarios in technical, empirical, and risk audit contexts, as documented in the referenced arXiv corpus (Potter et al., 2020, Dip et al., 2024, Ackerman et al., 9 Dec 2025, Ackerman et al., 9 Dec 2025).