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position: absolute; left: 15px; top: 12px; } } /* Style the buttons that are used to open and close the accordion panel */ #mobile-menu button.accordion, #mobile-menu button.no-accordion { letter-spacing: 1px; text-align: left; color: #4148C2; font-size: 1.1em; font-weight: 700 !important; font-family: 'lato', helvetica, sans-serif; padding: 15px 0px 15px 0px; border-top: none !important; border-bottom: .5px solid rgb(221, 213, 199) !important; cursor: pointer; width: 100%; } .mega-menu__content .accordion { border: 0; font-weight: 700; } #mobile-menu .accordion:after { content: '\02795'; /* Unicode character for "plus" sign (+) */ font-size: 13px; color: #777; float: right; margin-left: 5px; } #mobile-menu button.active:after { content: "\2796"; /* Unicode character for "minus" sign (-) */ } /* Add a background color to the button if it is clicked on (add the .active class with JS), and when you move the mouse over it (hover) */ #mobile-menu button.active, #mobile-menu button.accordion:hover { background-color: transparent; } /* Style the accordion panel. Note: hidden by default */ #mobile-menu .panel { padding: 0 0; background-color: transparent; max-height: 0; box-shadow: 0 !important; overflow: hidden; transition: max-height 0.2s ease-out; margin: 0px !important; border: 0 !important; } #mobile-menu .panel ul li { margin: 0px 0px; padding-left: 0px; } #mobile-menu .panel ul { margin-top: 10px; margin-bottom: 10px; padding-left: 0px; } #mobile-menu .panel ul li { line-height: 2; } #mobile-menu .panel ul li a { color: #000; text-decoration: underline; cursor: pointer; } .controls__menu-mobile { text-transform: uppercase; } .controls__menu-mobile svg { fill: #222; fill: var(--color-neutral--charcoal); width: 1.75rem; height: 1.75rem; } .cart-badge { display: inline-block; text-align: center; line-height: 1; background: #7899F2; color: white; font-weight: bold; border-radius: 20px; padding: 3px 6px; margin-left: 3px; } @media screen and (min-width: 65em) { #mobile-menu .controls__menu { display: inline; } } @media screen and (max-width: 65em) { #mobile-menu .controls__menu svg { display: inline; } } /*NAP mega menu dropdown*/ li.nap-mega-menu.mega-dropdown { position: static !important; width:100% !important; } .nap-mega-menu .mega-dropdown-menu { padding: 20px 0px; box-shadow: none; -webkit-box-shadow: none; margin-top: 10px !important; box-shadow: 0 5px 15px var(--box-shadow--charcoal); transform: none !important; top: 100% !important; } .nap-mega-menu .mega-dropdown-menu > li > ul.browse-col { padding: 0; margin: 0; width: 100%; } .nap-mega-menu .mega-dropdown-menu > li > ul > li { list-style: none; } .nap-mega-menu .mega-dropdown-menu > li > ul > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.428571429; color: #4148C2; white-space: normal; font-size: 14px; text-transform: none; } .nap-mega-menu .mega-dropdown-menu > li ul > li > a:hover, .nap-mega-menu .mega-dropdown-menu > li ul > li > a:focus { text-decoration: none; color: #444; background-color: #f5f5f5; } 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In particular, this chapter answers the second of the committee's three primary tasks, "consider examples of AI corruption under operational conditions and against malicious cyberattacks." 5.1 INTRODUCTION An AI-enabled system includes technical risks that must be considered during the test and evaluation (T&E)</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=102');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 103...</div><div class=foundblock>... <font class="btext">In addition, OT&E performance tests can ferret out operational environmental degrading ef fects, which may motivate model retraining using new datasets. Some attacks, referred to as backdoor attacks, only manifest once the adversary triggers them.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=103');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 104...</div><div class=foundblock>... <font class="btext">Instead, AI models learn to make predictions based on pat terns and relationships specific to the training data. As a result, they do not generalize well to novel data, and even a bit of extra signal noise in a sensor collection, which might be invisible to the human eye, can potentially confuse an AI system.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=104');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 105...</div><div class=foundblock>... <font class="btext">Thus, while this report focuses specifically on the question of adversarial attacks against the AI components, it is important to con tinue to test and evaluate AI-enabled systems against a broad spectrum of adversaries and attacks in a realistic operational context. In addition to development and test datasets, the collection and use of operational data, especially in training AI compo nents, is key to mitigating threats to the AI component and the system as a whole.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=105');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 106...</div><div class=foundblock>... <font class="btext">This may be in the form of statistical measures such as precision and recall, reduction of a performance envelope required for a mission objective, or in a violation of the system's security requirements, such as maintaining the secrecy BOX 5-1 Some Examples of Adversarial Attacks Against AI Systemsa Adversarial examples: These are inputs to a machine learning model that are specifically designed to mislead the model and cause it to make incorrect predictions. Adversarial examples can be created by adding small, imperceptible perturbations to the input data designed to fool the model.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=106');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 107...</div><div class=foundblock>... <font class="btext">Thus, all traditional testing for adversarial attacks against the attack surface that has been previously defined is still required. Defensive mechanisms that obvi ate or limit the capability of an adversary to take advantage of an attack surface are also the first line of defense for AI-enabled components.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=107');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 108...</div><div class=foundblock>... <font class="btext">AI model inversion attacks refer to techniques that aim to reverse engineer the internal workings of an AI model. These attacks are a type of adversarial attack in which an attacker seeks to reconstruct the input data or features used to train a model or to generate synthetic inputs that will produce a desired output from the model.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=108');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 109...</div><div class=foundblock>... <font class="btext">In summary, the attack surface of AI-enabled systems encompasses all tra ditional attack surfaces inherent in software-intensive systems -- especially those of ML-enabled systems -- but has the additional considerations of AI corruption through the life cycle of the AI system, including the data used in training, test, and operations, and the access to the details in any configuration or model that is part of the AI component. 5.5 RISK OF ADVERSARIAL ATTACKS It can be useful to divide the risk of adversarial attacks into the different levels of integration of the AI-enabled system.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=109');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 110...</div><div class=foundblock>... <font class="btext">Note, however, that over-training in the adversarial space could create vulnerabilities in the inversion attack of the resulting model, thus exposing which attacks and training sets are used to an adversary with limited access to the resulting deployed system. Recognizing the planned mission objectives is also important when testing these components.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=110');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 111...</div><div class=foundblock>... <font class="btext">Palat, and A Sinha, 2022, "Measuring AI Systems Beyond Accuracy," paper presented at AAAI Spring Symposium Series Workshop on AI Engineering: Creating Scalable, Human-Centered and Robust AI Systems, https://doi.org/10.48550/ arXiv.2204.04211.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=111');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 112...</div><div class=foundblock>... <font class="btext">In addition, network security, data protection, encryp tion, and other classic defenses must be integrated at this point into the operational system and tested as part of the OT&E process. Finally, the risk of adversarial attack during deployment is increased over the traditional software-intensive systems by creating an additional attack surface FIGURE 5-2 The operational environment for ML-enabled systems.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=112');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 113...</div><div class=foundblock>... <font class="btext">The execution of Zero Trust provides targeteers trusted, tagged, and labeled data so they can confi dently employ and share it with trusted partners, assured that the data is protected, secure, and accessed by only the people who need it when they need it, using least privilege principles.5 5 DoD, 2022, "Zero Trust Strategy." </font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=113');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 114...</div><div class=foundblock>... <font class="btext">In addition, using specific APIs to control data flow and access to the AI life cycle and deployed environments will limit the ability of the adversary to propagate attacks to multiple operational systems. Note this is also a trade-off to a dynamic approach to DevSecOps that would enable near real-time updates to operational models based on retraining and updates on changing deployed environments as well as the use of operational data in the development and training of new models.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=114');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 115...</div><div class=foundblock>... <font class="btext">Finding 5-3: The impact of adversarial attacks on human-AI enabled systems has not been well understood. At present, the DoD Zero Trust Strategy is only being implemented on en terprise systems.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=115');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 116...</div><div class=foundblock>... <font class="btext">The Department's ability to protect, and by extension, DoD personnel against the array of increasingly sophisticated cybersecurity threats depends on it. 5.7 ROBUST AND SECURE AI MODELS A common approach for increasing the robustness and security of AI models is the incorporation of monitoring or watchdog systems that compare the output of an AI-enabled system to pre-defined operational limits.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=116');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 117...</div><div class=foundblock>... <font class="btext">In particular, attacks that require unimpeded access to an underlying AI model are unlikely to be practical with traditional protections and mitigations inherent in deployed DAF systems. Nevertheless, as stated 7 Variations regarding adversarial attacks were discussed in Section 3; in this section robustness also includes both adversarial and non-adversarial variations in the environment.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=117');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 118...</div><div class=foundblock>... <font class="btext">These may include model inversion attacks and environmental manipulation attacks that exploit the mechanisms of the AI component without direct access. Even attacks that require unimpeded access to the AI model may be successful due to the transferability of adversarial attacks.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=118');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 119...</div><div class=foundblock>... <font class="btext">In many cases, sensitive data would need to be sanitized to prevent training data from exposure during model inver sion or when testing new types of AI-enabled systems. However, current approaches to sanitization do not effectively support the trade-off between the effective training of the system and the possibility of leaking sensitive data.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=119');">Read the entire page → </a></div></td></tr><tr bgcolor=white><td valign=top><div class=fromblock>From page 120...</div><div class=foundblock>... <font class="btext">120 Test and E va l u at i o n C h a l l e n g e s in AI - E n a b l e d S y s t e m s Conclusion 5-1: Promising areas of research that will improve the mitigation of adversarial AI include techniques for data sanitization, quantifiable uncertainty, and certifiable robustness. Additionally, Chapter 6 discusses additional emerging AI technologies and promising areas of research.</font> </div><div class=seepage><a href="javascript:poe('http://nap.nationalacademies.org/openbook.php?record_id=27092&page=120');">Read the entire page → </a></div></td></tr> </table> </div> </div> <div class="col-xs-3 col-md-2 pull-right termlist"> <p class="small">Key Terms</p> <li class="xy"><a href="javascript:x_adversarial_attacks();">adversarial attacks</a></li><li class="xy"><a href="javascript:x_zero_trust();">zero trust</a></li><li class="xy"><a href="javascript:x_training_data();">training data</a></li><li class="xy"><a href="javascript:x_operational_conditions();">operational conditions</a></li><li class="xy"><a href="javascript:x_operational_data();">operational data</a></li><div class=ider><br></div> <li class="xy"><a href="javascript:x_model_inversion();">model inversion</a></li><li class="xy"><a href="javascript:x_attacks_involve();">attacks involve</a></li><li class="xy"><a href="javascript:x_machine_learning();">machine learning</a></li><li class="xy"><a href="javascript:x_trust_strategy();">trust strategy</a></li><li class="xy"><a href="javascript:x_supply_chain();">supply chain</a></li><div class=ider><br></div> <li class="xy"><a href="javascript:x_adversarial_attack();">adversarial attack</a></li><li class="xy"><a href="javascript:x_traditional_attack();">traditional attack</a></li><li class="xy"><a href="javascript:x_operational_environments();">operational environments</a></li><li class="xy"><a href="javascript:x_software_component();">software component</a></li><li class="xy"><a href="javascript:x_learning_model();">learning model</a></li><div class=ider><br></div> <li class="xy"><a href="javascript:x_inversion_attacks();">inversion attacks</a></li><li class="xy"><a href="javascript:x_input_data();">input data</a></li><li class="xy"><a href="javascript:x_dod_zero();">dod zero</a></li><li class="xy"><a href="javascript:x_cyberattack_surface();">cyberattack surface</a></li><li class="xy"><a href="javascript:x_require_unimpeded();">require unimpeded</a></li><div class=ider><br></div> <li class="xy"><a href="javascript:x_adversarial_examples();">adversarial examples</a></li><li class="xy"><a href="javascript:x_adversarial_manipulation();">adversarial manipulation</a></li><li class="xy"><a href="javascript:x_data_sanitization();">data sanitization</a></li><li class="xy"><a href="javascript:x_distribution_shifts();">distribution shifts</a></li><li class="xy"><a href="javascript:x_test_datasets();">test datasets</a></li><div class=ider><br></div> <li class="xy"><a href="javascript:x_network_security();">network security</a></li><li class="xy"><a href="javascript:x_unimpeded_access();">unimpeded access</a></li><li class="xy"><a href="javascript:x_model_performance();">model performance</a></li><li class="xy"><a href="javascript:x_attack_surfaces();">attack surfaces</a></li><li class="xy"><a 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