Program Atlas dossier
BENGAL
Research into methods for characterizing and mitigating threat modes in large language models, including unsupported outputs, sensitive-information aggregation, and poisoned information sources.
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- Reading time
- 2 min
- Record type
- Program Atlas dossier
- Revised
- Content owner
- Program Atlas editorial team
- Next review
Program identity
#- Public program name
- Biases, Threats, and Vulnerabilities in Large Language Models
- Accepted acronym
- BENGAL
- Responsible organization
- Intelligence Advanced Research Projects Activity
Public purpose
#Research into methods for characterizing and mitigating threat modes in large language models, including unsupported outputs, sensitive-information aggregation, and poisoned information sources.
Established public facts
#- The program is publicly associated with evaluation of large-language-model threat modes.
- Relevant risk categories include hallucinated content, source poisoning, and the aggregation of sensitive information.
- The educational value lies in measurable evaluation rather than treating model confidence as proof.
Technical approach
#- Evidence provenance
- Hallucination triage
- Data-poisoning indicators
- Confidence assessment
Evaluation and limits
#Evaluation concept
Public program descriptions emphasize measurable evaluation against defined research objectives; this portal does not reproduce restricted metrics or implementation details.
What remains uncertain
Program details, performer status, and lifecycle milestones can change. This build preserves only a high-level educational summary.
Ethics, privacy, and security
#Ethics
The educational treatment emphasizes proportionality, consent, error analysis, dual-use risk, and meaningful human review.
Privacy
The portal uses synthetic examples and does not collect biometric, location, communications, or identity data for these lessons.
Security
No current capabilities, targets, facilities, credentials, or operational procedures are published.
Important vocabulary
#- Large Language Model
- Hallucination
- Data poisoning
- Source validation
- Confidence assessment
Why this program matters
#Research into methods for characterizing and mitigating threat modes in large language models, including unsupported outputs, sensitive-information aggregation, and poisoned information sources.
End factual context · Begin fictional application
Model Integrity Desk
#Players triage synthetic model outputs, identify unsupported claims, trace evidence provenance, and assign calibrated confidence without touching real personal or operational data.
Simulation mechanics
- Evidence provenance
- Hallucination triage
- Data-poisoning indicators
- Confidence assessment
This section does not describe a real organization’s actions, current capability, target, facility, or operation.
