The Tool Desk
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What O’Brien meant by “coming in”
O’Brien, who was then the Air Force deputy chief of staff for intelligence, surveillance, reconnaissance and cyber, was warning about the inputs on which an AI-supported process depends. Those inputs can include training examples, live sensor feeds, telemetry, labels, software libraries, model parameters and the code that turns data into recommendations.
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Her concern was blunt: “If our adversary is able to inject uncertainty into any part of that process, we’re kind of dead in the water.” The statement describes a risk model, not a report that a particular system had already been breached. In the 2021 conference report, the danger was that an opponent could make a system less trustworthy before an operator sees its output.
How an inbound attack can change an AI result
Poisoning training data
An attacker can seek to alter the examples used to train a model. Incorrect labels, fabricated records or selectively missing cases can teach the model a distorted relationship between a signal and the conclusion it is expected to produce. A model may then appear to perform normally while failing on situations the attacker has shaped.
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Manipulating operational data
During live use, adversaries may tamper with sensor observations, telemetry or other feeds. Even small changes can create an anomalous picture for a system that fuses many sources. The aim need not be to take over the entire network; introducing uncertainty into one important part of the chain may be enough to affect a recommendation.
Altering code or algorithms
The attack surface also includes the software and mathematical logic around a model. Changes to preprocessing, feature extraction, decision thresholds or update routines can steer an output without looking like a conventional data breach.
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Exploiting anomalies
Machine-learning systems can react unpredictably to inputs that differ from their training distribution. An adversary can search for such weak points through data collection and experimentation. The report noted that some machine-learning vulnerabilities may not be fixable with an ordinary software patch and could be reached without an insider’s direct access to a protected network.
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Ed Vasko of Boise State University’s Institute of Pervasive Cybersecurity argued that expanding telemetry creates more data and more information environments for an adversary to attack. Every additional collection point can improve visibility for defenders, but it also adds another source whose provenance, integrity and timing must be checked.
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This does not mean inbound manipulation has replaced data theft in every environment. It means confidentiality is only one security objective. An organization can prevent exfiltration and still make a dangerous decision if the information arriving at the model is false.
Why military decision systems face unusual stakes
The 2021 report placed the issue in the context of military applications, including rapid target identification and decision support in contested environments. When a machine processes information at operational speed, altered inputs can influence a decision on the same timescale. A human may have too little time to reconstruct where the uncertainty entered.
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The National Security Commission on Artificial Intelligence summarized the broader warning quoted in the report: “the threat is not hypothetical: Adversarial attacks are happening.” The report did not provide a single breach statistic or claim that O’Brien’s warning described one named incident; it presented an emerging technical and organizational challenge.
AI security versus conventional IT security
| Question | Conventional IT emphasis | AI-system emphasis |
|---|---|---|
| What is attacked? | Networks, accounts and stored data | Training data, live inputs, models and algorithms |
| When can it happen? | Often after deployment or during access | During collection, labeling, training, updates and live inference |
| Can it be patched? | A software fix may remove a known flaw | Some learned weaknesses can persist in data or model behavior and require retraining, replacement or process changes |
| How much oversight is needed? | Human review is commonly tied to alerts and access events | People must also judge data provenance, model confidence and whether an output is plausible |
| How fast is the impact? | Often limited by human-paced workflows | Potentially as fast as automated sensing, fusion and recommendation |
What a defensive program needs
Test the whole pipeline with AI Red Teams
Dedicated AI Red Teams should challenge data collection, labeling, training, deployment and monitoring—not just perimeter controls. Their exercises can look for poisoning opportunities, anomalous inputs, unsafe assumptions and ways to induce confident but incorrect outputs.
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Make provenance and behavior visible
Systems should record where data came from, when it changed, which transformation produced a feature and which model version generated an output. Transparent confidence indicators and audit trails give operators a chance to recognize uncertainty instead of treating every recommendation as fact.
Build redundancy and resilience
Independent sensors, diverse data sources and redundant components can prevent one compromised feed from determining the result. Redundancy is useful only when the sources are genuinely independent; copying the same poisoned data into several systems does not create protection.
Keep intervention points for people
Human operators need authority and practical time to pause, reject or seek corroboration for a high-consequence recommendation. Oversight should be designed into the workflow, with clear escalation rules for conflicting sources, unusual inputs or low confidence.
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- Inventory every external and internal data source that can influence a model, including update channels and third-party labels.
- Define integrity checks and provenance requirements before data is accepted for training or operations.
- Monitor for distribution shifts and unexplained changes in model behavior, not only unauthorized logins.
- Separate model development, approval and production update privileges.
- Exercise failure scenarios in which one feed is manipulated or unavailable.
- Document when a human must verify an output and how the system fails safely if verification is impossible.
O’Brien’s “flip” is therefore a change in defensive focus: protect against information leaving a system, but also protect the information and logic entering it. For AI used in consequential decisions, trustworthy inputs, resilient design and meaningful human intervention are parts of security—not optional additions after deployment.
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