IARPA-RFI--23-03_revised_08142023_FINAL C.pdf

Request for Information – Characterizing Large Language Model Biases, Threats and Vulnerabilities

RFI Number: IARPA-RFI-23-03 Agency: Office of the Director of National Intelligence Office: Intelligence Advanced Research Projects Activity – Office of Analysis

IARPA-RFI-23-03

Request for Information (RFI): Characterizing Large Language Model biases, Threats and Vulnerabilities

IARPA is seeking information on established characterizations of vulnerabilities and threats that could impact the safe use of large language models (LLMs) by intelligence analysts. This RFI is issued for planning purposes only, and it does not constitute a formal solicitation for proposals or suggest the procurement of any material, data sets, etc. The following sections of this announcement contain details on the specific technology areas of interest, along with instructions for the submission of responses.

Background and Scope:

LLMs have received much public attention recently due, among other things, to their human-like interaction with users. These capabilities promise to substantially transform and enhance work across sectors in the coming years. However, LLMs have been shown to exhibit erroneous and potentially harmful behavior, posing threats to the end-users.

This RFI aims to elicit frameworks to categorize and characterize vulnerabilities and threats associated with LLM technologies, specifically in the context of their potential use in intelligence analysis.

Responses to this RFI should answer any or all of the following questions:

1. Does your organization have a framework for classifying and understanding the range of LLM threats and/or vulnerabilities? If so, please describe this framework, and briefly articulate each threat and/or vulnerability and its risks.

2. Has your organization identified specific LLM threats and vulnerabilities that are not well-characterized by prior taxonomies (c.f., “OWASP Top 10 for LLM” [1])? If so, please provide specific descriptions of each such threat and/or vulnerability and its impacts.

3. Does your organization have any novel methods to detect or mitigate threats to users posed by LLM vulnerabilities?

4. Does your organization have novel methods to quantify confidence in LLM outputs?

For the purpose of this RFI, IARPA is interested in the characterizations and methods for both "white box" models (some privileged access to parameters or code) and "black box" models (no privileged access to parameters and code).

Preparation Instructions to Respondents:

IARPA requests that submittals briefly and clearly describe the approach or capability, directly address any or all of the specific questions, and outline any known critical technical issues/obstacles. This announcement contains all of the information required to submit a response. No additional forms, kits, or other materials are needed.

IARPA welcomes responses from all capable and qualified sources from within and outside of the U.S.

Because IARPA is interested in an integrated approach, responses from teams with complementary areas of expertise are encouraged.

Submissions from Federally Funded Research and Development Centers (FFRDCs) and University Affiliated Research Centers (UARCs) are permitted but with an understanding that neither groups are able to propose against any future IARPA program. Instead, any submissions from these groups should consider the technical elements described above but include reflection upon how they would support program efforts as a potential test and evaluation partner enabling IARPA to validate different potential approaches to meet the research challenge.

Responses have the following formatting requirements:

1. A one-page cover sheet that identifies the title, organization(s), respondent's technical and administrative points of contact - including names, addresses, phone and fax numbers, and email addresses of all co-authors, and clearly indicating its association with RFI-23-03;

2. A substantive, focused, one-half page executive summary;

3. Answers to the above questions including potential research approaches capable of achieving a potential program on this topic limited to 5 pages in minimum 12-point Times New Roman font, appropriate for single-sided, single-spaced 8.5 by 11-inch paper, with 1-inch margins);

4. A list of citations (any significant claims or reports of success must be accompanied by citations);

5. Optionally, a single overview briefing chart graphically depicting the key ideas;

6. An appendix of critical reference papers or white papers (no more than 3) associated with answers or potential approaches.

Submission Instructions to Respondents:

Responses to this RFI are due no later than 5 p.m., Eastern Time, on 21 August, 2023. All submissions must be electronically submitted to dni-iarpa-rfi-23-03@iarpa.gov as a PDF document. Inquiries to this RFI must be submitted to dni-iarpa-rfi-23-03@iarpa.gov. Do not send questions with proprietary content. No telephone inquiries will be accepted.

Disclaimers and Important Notes:

This is an RFI issued solely for information and planning purposes and does not constitute a solicitation. Respondents are advised that IARPA is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted under this RFI.

Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract. Respondents are solely responsible for all expenses associated with responding to this RFI. IARPA will not provide reimbursement for costs incurred in responding to this RFI. It is the respondent's responsibility to ensure that the submitted material has been approved for public release by the information owner.

The Government does not intend to award a contract on the basis of this RFI or to otherwise pay for the information solicited, nor is the Government obligated to issue a solicitation based on responses received. Neither proprietary nor classified concepts or information should be included in the submittal. However, should a respondent wish to submit classified concepts or information, prior coordination must be made with the IARPA Chief of Security. Email the Primary Point of Contact with a request for coordination with the IARPA Chief of Security.

Input on technical aspects of the responses may be solicited by IARPA from non-Government consultants/experts who are bound by appropriate non-disclosure requirements. Submissions may be reviewed and followed up on by an assigned technical contractor supporting the designated IARPA POC.

Several key laws, enacted over the past three decades, provide general privacy and confidentiality requirements that either directly or indirectly affect all government agencies.

These include The Privacy Act of 1974, The Computer Security Act of 1987, Health Insurance Portability and Accountability Act of 1996 (HIPAA), US Patriot Act of 2001, and The Confidential Information Protection and Statistical Efficiency Act of 2002. Under federal law, protected characteristics include race, color, national origin, religion, gender (including pregnancy), disability, age (if the employee is at least 40 years old), and citizenship status. Processing any of these data sets could inadvertently cause discrimination or bias, of these protected characteristics.

Contracting Office Address:

Office of the Director of National Intelligence Intelligence Advanced Research Projects Activity Washington, District of Columbia 20511 United States

Primary Point of Contact:

Dr. Timothy Mckinnon Program Manager - Office of Analysis Research Intelligence Advanced Research Projects Activity

Sources:

[1] OWASP Top 10 List for Large Language Models. Viewed on 28 July, 2023