Each January on the SEI Weblog, we current the ten most-visited posts of the earlier yr. This yr’s high 10 highlights our work in quantum computing, software program modeling, massive language fashions, DevSecOps, and synthetic intelligence. The posts, which have been printed between January 1, 2023, and December 31, 2023, are introduced under in reverse order primarily based on the variety of visits.
#10 Contextualizing Finish-Consumer Wants: Tips on how to Measure the Trustworthiness of an AI System
by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner
As potential functions of synthetic intelligence (AI) proceed to increase, the query stays: will customers need the know-how and belief it? How can innovators design AI-enabled merchandise, providers, and capabilities which can be efficiently adopted, relatively than discarded as a result of the system fails to satisfy operational necessities, corresponding to end-user confidence? AI’s promise is sure to perceptions of its trustworthiness.
To highlight a number of real-world eventualities, think about:
- How does a software program engineer gauge the trustworthiness of automated code era instruments to co-write practical, high quality code?
- How does a physician gauge the trustworthiness of predictive healthcare functions to co-diagnose affected person situations?
- How does a warfighter gauge the trustworthiness of computer-vision enabled risk intelligence to co-detect adversaries?
What occurs when customers don’t belief these programs? AI’s skill to efficiently associate with the software program engineer, physician, or warfighter in these circumstances depends upon whether or not these finish customers belief the AI system to associate successfully with them and ship the result promised. To construct applicable ranges of belief, expectations have to be managed for what AI can realistically ship.
This weblog submit explores main analysis and classes realized to advance dialogue of measure the trustworthiness of AI so warfighters and finish customers normally can understand the promised outcomes.
Learn the submit in its entirety.
#9 5 Greatest Practices from Business for Implementing a Zero Belief Structure
by Matthew Nicolai, Nathaniel Richmond, and Timothy Morrow
Zero belief (ZT) structure (ZTA) has the potential to enhance an enterprise’s safety posture. There may be nonetheless appreciable uncertainty in regards to the ZT transformation course of, nonetheless, in addition to how ZTA will in the end seem in observe. Latest govt orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and lots of non-public sector organizations are following go well with. In response to those govt orders, researchers at the SEI’s CERT Division hosted Zero Belief Business Days in August 2022 to allow trade stakeholders to share details about implementing ZT.
On this weblog submit, which we tailored from a white paper, we element 5 ZT greatest practices recognized through the two-day occasion, focus on why they’re vital, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.
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#8 The Problem of Adversarial Machine Studying
by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge
Think about using to work in your self-driving automotive. As you method a cease signal, as a substitute of stopping, the automotive hastens and goes by means of the cease signal as a result of it interprets the cease signal as a velocity restrict signal. How did this occur? Despite the fact that the automotive’s machine studying (ML) system was skilled to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automotive into considering it was a 45-mph velocity restrict signal. This easy act of placing stickers on a cease signal is one instance of an adversarial assault on ML programs.
On this SEI Weblog submit, I look at how ML programs will be subverted and, on this context, clarify the idea of adversarial machine studying. I additionally look at the motivations of adversaries and what researchers are doing to mitigate their assaults. Lastly, I introduce a primary taxonomy delineating the methods through which an ML mannequin will be influenced and present how this taxonomy can be utilized to tell fashions which can be sturdy in opposition to adversarial actions.
Learn the submit in its entirety.
#7 Play it Once more Sam! or How I Discovered to Love Giant Language Fashions
by Jay Palat
“AI won’t substitute you. An individual utilizing AI will.”
-Santiago @svpino
In our work as advisors in software program and AI engineering, we are sometimes requested in regards to the efficacy of AI code assistant instruments like Copilot, GhostWriter, or Tabnine primarily based on massive language mannequin (LLM). Latest innovation within the constructing and curation of LLMs demonstrates highly effective instruments for the manipulation of textual content. By discovering patterns in massive our bodies of textual content, these fashions can predict the following phrase to put in writing sentences and paragraphs of coherent content material. The priority surrounding these instruments is powerful – from New York faculties banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many functions are strictly restricted to writing textual content, a number of functions discover the patterns to work on code, as properly. The hype surrounding these functions ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to concern, uncertainty, and doubt (“LLMs are going to take my job”). Within the Communications of the ACM, Matt Welsh goes as far as to declare we’ve reached “The Finish of Programming.” Whereas built-in growth environments have had code era and automation instruments for years, on this submit I’ll discover what new developments in AI and LLMs imply for software program growth.
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#6 Tips on how to Use Docker and NS-3 to Create Sensible Community Simulations
by Alejandro Gomez
Generally, researchers and builders have to simulate numerous kinds of networks with software program that will in any other case be onerous to do with actual gadgets. For instance, some {hardware} will be onerous to get, costly to arrange, or past the talents of the workforce to implement. When the underlying {hardware} will not be a priority however the important capabilities that it does is, software program could be a viable various.
NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer time of Code, and others. It has a excessive diploma of functionality to simulate numerous sorts of networks and user-end gadgets, and its Python-to-C++ bindings make it accessible for a lot of builders.
In some instances, nonetheless, it is not enough to simulate a community. A simulator would possibly want to check how information behaves in a simulated community (i.e., testing the integrity of Consumer Datagram Protocol (UDP) visitors in a Wi-Fi community, how 5G information propagates throughout cell towers and consumer gadgets, and so forth. NS-3 permits such sorts of simulations by piping information from faucet interfaces (a characteristic of digital community gadgets offered by the Linux kernel that move ethernet frames to and from consumer house) into the operating simulation.
This weblog submit presents a tutorial on how one can transmit reside information by means of an NS-3-simulated community with the added benefit of getting the data-producing/data-receiving nodes be Docker containers. Lastly, we use Docker Compose to automate advanced setups and make repeatable simulations in seconds.
Learn the submit in its entirety.
#5 5 Challenges to Implementing DevSecOps and Tips on how to Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the challenge degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Not too long ago, nonetheless, the dialogue has shifted to this system degree to align safety with enterprise aims. The perfect consequence of such a shift is one through which software program growth groups act in alignment with enterprise targets, organizational threat, and answer architectures, and these groups perceive that safety practices are integral to enterprise success. DevSecOps, which builds on DevOps ideas and locations extra give attention to safety actions all through all phases of the software program growth lifecycle (SDLC), may help organizations understand this splendid state. Nevertheless, the shift from project- to program-level considering raises quite a few challenges. In our expertise, we’ve noticed 5 frequent challenges to implementing DevSecOps. This SEI Weblog submit articulates these challenges and gives actions organizations can take to beat them.
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#4 Utility of Giant Language Fashions (LLMs) in Software program Engineering: Overblown Hype or Disruptive Change?
by Ipek Ozkaya, Anita Carleton, John E. Robert, and Douglas Schmidt (Vanderbilt College)
Has the day lastly arrived when massive language fashions (LLMs) flip us all into higher software program engineers? Or are LLMs creating extra hype than performance for software program growth, and, on the similar time, plunging everybody right into a world the place it’s onerous to tell apart the superbly fashioned, but typically faux and incorrect, code generated by synthetic intelligence (AI) packages from verified and well-tested programs?
This weblog submit, which builds on concepts launched within the IEEE paper Utility of Giant Language Fashions to Software program Engineering Duties: Alternatives, Dangers, and Implications by Ipek Ozkaya, focuses on alternatives and cautions for LLMs in software program growth, the implications of incorporating LLMs into software-reliant programs, and the areas the place extra analysis and improvements are wanted to advance their use in software program engineering.
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#3 Rust Vulnerability Evaluation and Maturity Challenges
by Garret Wassermann and David Svoboda
Whereas the reminiscence security and security measures of the Rust programming language will be efficient in lots of conditions, Rust’s compiler could be very explicit on what constitutes good software program design practices. Each time design assumptions disagree with real-world information and assumptions, there may be the potential of safety vulnerabilities–and malicious software program that may reap the benefits of these vulnerabilities. On this submit, we are going to give attention to customers of Rust packages, relatively than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is on the market or not. These instruments are necessary for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on doable instructions through which instruments and automatic code evaluation can enhance. We additionally touch upon the maturity of the Rust software program ecosystem as an entire and the way that may impression future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Heart (CERT/CC). This submit is the second in a collection exploring the Rust programming language. The first submit explored safety points with Rust.
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#2 Software program Modeling: What to Mannequin and Why
by John McGregor and Sholom G. Cohen
Mannequin-based programs engineering (MBSE) environments are supposed to help engineering actions of all stakeholders throughout the envisioning, growing, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE atmosphere, help efforts such because the automation of standardized evaluation strategies by all stakeholders and the upkeep of a single authoritative supply of fact about product info. The mannequin faithfully represents the ultimate product in these attributes of curiosity to varied stakeholders. The result’s an general discount of growth dangers.
When initially envisioned, the necessities for a product could appear to symbolize the correct product for the stakeholders. Throughout growth, nonetheless, the as-designed product involves replicate an understanding of what’s actually wanted that’s superior to the unique set of necessities. When it’s time to combine parts, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is now not represented and is now not a legitimate supply of check instances. Many questions come up, corresponding to
- How do I consider the failure of a check?
- How can I consider the completeness of a check set?
- How do I monitor failures and the fixes utilized to them?
- How do I do know that fixes utilized don’t break one thing else?
Such is the case with necessities, and far the identical ought to be the case for a set of fashions created throughout growth—are they nonetheless consultant of the carried out product present process integration?
One of many targets for sturdy design is to have an up-to-date single authoritative supply of fact through which discipline-specific views of the system are created utilizing the identical mannequin parts at every growth step. The one authoritative supply will typically be a group of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a legitimate supply of full and proper verification and validation (V&V) actions. On this submit, we look at the questions above and different questions that come up throughout growth and use the solutions to explain modeling and evaluation actions.
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#1 Cybersecurity of Quantum Computing: A New Frontier
by Tom Scanlon
Analysis and growth of quantum computer systems continues to develop at a speedy tempo. The U.S. authorities alone spent greater than $800 million on quantum info science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they’ll have the ability to clear up sure issues which can be classically intractable, that means a standard laptop can’t full the calculations inside human-usable timescales. Given this computational energy, there may be rising dialogue surrounding the cyber threats quantum computer systems could pose sooner or later. As an example, Alejandro Mayorkas, secretary of the Division of Homeland Safety, has recognized the transition to post-quantum encryption as a precedence to make sure cyber resilience. There may be little or no dialogue, nonetheless, on how we are going to shield quantum computer systems sooner or later. If quantum computer systems are to turn into such beneficial belongings, it’s affordable to challenge that they’ll finally be the goal of malicious exercise.
I used to be just lately invited to be a participant within the Workshop on Cybersecurity of Quantum Computing, co-sponsored by the Nationwide Science Basis (NSF) and the White Home Workplace of Science and Expertise Coverage, the place we examined the rising subject of cybersecurity for quantum computing. Whereas quantum computer systems are nonetheless nascent in some ways, it’s by no means too early to handle looming cybersecurity issues. This submit will discover points associated to creating the self-discipline of cyber safety of quantum computing and description six areas of future analysis within the subject of quantum cybersecurity.
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Trying Forward in 2024
We publish a brand new submit on the SEI Weblog each Monday morning. Within the coming months, search for posts highlighting the SEI’s work in synthetic intelligence, cybersecurity, and edge computing.
