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There is no proven, universal winner. Current assessments lean toward a possible attacker advantage in the near term because AI can compress reconnaissance, vulnerability discovery, exploit development and social-engineering workflows quickly. Defensive work must be integrated across complex systems while preserving uptime and stopping intrusions, so deployment is harder. That is an expert forecast, not evidence that attackers routinely beat defenders. A small controlled capture-the-flag (CTF) study even found defensive agents outperforming offensive agents on unconstrained tasks, with that edge disappearing when availability and intrusion-prevention requirements were added. Over the longer term, continuous AI-assisted defense, better software architecture, coordination and trustworthy measurement could shift the balance toward defenders.
What does “AI advantage” mean in cybersecurity?
“Advantage” can mean that one side performs a task faster, more cheaply or at greater scale; it does not automatically mean that side wins real incidents. Comparing attacker and defender AI requires the same task, environment, model access, data, expertise, time limit and definition of success.
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| Comparison question | Why it changes the answer |
|---|---|
| What stage is being measured? | Reconnaissance, vulnerability discovery, exploitation, detection, patching and recovery have different automation barriers. |
| Capability or operational outcome? | A model demonstrating a technique in a laboratory is not the same as preventing a breach in a production network. |
| What counts as defensive success? | Patching a flaw is easier to score than patching it while maintaining availability and blocking an intrusion. |
| Who has access? | Results depend on models, compute, proprietary data, telemetry, cloud permissions and human security expertise. |
| Where was it tested? | CTF battlegrounds, isolated labs, enterprises, cloud estates and critical infrastructure impose different constraints. |
| How reliable is the estimate? | Sample size, model version, repeated trials and uncertainty determine whether a result generalizes. |
The Center for Security and Emerging Technology (CSET) analysis published in May 2025 summarizes the central point: “Rather than a single answer, there are many ways that AI will help both cyber attackers and defenders.”
Which side appears ahead in the near term?
The strongest current forecast gives attackers a plausible near-term edge, but it is an assessment of workflow dynamics rather than a measured universal outcome. A June 2026 rapid expert consultation from the National Academies says many offensive workflows can be compressed quickly, while defensive work has to be integrated across complicated organizations and performed without disrupting service.
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That asymmetry matters. An attacker may need one useful path into one target; a defender must maintain trustworthy controls across identities, endpoints, applications, cloud services, suppliers and backups. A faster offensive step can therefore create pressure even when defensive tools are improving. The consultation identifies reconnaissance, finding vulnerabilities, developing exploits and conducting social engineering as areas that could become faster, cheaper or more scalable with AI.
The statement should be read as an expectation about current incentives and technical friction. It is not a claim that AI-powered attackers always succeed, that every organization is equally exposed, or that AI can autonomously run sophisticated campaigns from end to end.
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How can AI compress offensive workflows?
AI can reduce the time between an attacker’s question and a usable lead. The degree of advantage depends on access to target information, quality of the model, safeguards, human review and whether the resulting output works against the real system.
Reconnaissance
Systems can help collect, organize and summarize public or acquired information about people, technology stacks and exposed services. Faster triage may let a small team examine more potential targets, but useful reconnaissance still depends on current data and accurate interpretation. An automatically generated list is not proof that a service is exploitable.
Vulnerability discovery
Models can assist with code review, test generation and prioritization of suspicious behavior. The likely benefit is speed and scale in searching for candidate weaknesses. False positives, incomplete source code and unusual production configurations can leave humans responsible for validating whether a finding is real and exploitable.
Exploit development
AI may help translate technical descriptions into proof-of-concept code or adapt known techniques to a particular environment. That capability can shorten preparation time, yet a laboratory exploit may fail because of authentication, segmentation, patches, mitigations or application-specific behavior.
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Social engineering
Language and image generation can make messages more tailored and increase the volume of attempted lures. Success still depends on delivery, recipient behavior, authentication controls and reporting. The existence of persuasive content should not be treated as evidence of a completed compromise.
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These examples describe assistance with parts of an attack chain. The International Scientific Report on the Safety of Advanced AI, updated October 22, 2025, stresses that dual-use capability and access asymmetry matter: an attacker advantage is more plausible when AI automates offensive tasks without equivalent defensive tasks, or when advanced systems are available to attackers but not defenders.
How does AI strengthen cybersecurity defense?
AI can improve defense by helping people understand large volumes of telemetry, test systems continuously and respond more consistently. CSET’s 2025 analysis finds many ways AI can help both sides and identifies actions that could improve the defensive position; it is an analytic assessment, not a definitive experiment.
Detection and triage
Models can summarize alerts, connect events across systems and help analysts investigate unusual activity. The practical gain is reduced time spent on repetitive analysis, provided telemetry is complete and the model’s explanations are checked.
Vulnerability management
AI can prioritize flaws, propose patches and identify related code or configurations. A patch that is technically correct but breaks a production dependency is not a successful defense, so testing and staged deployment remain necessary.
Continuous testing and modeling
Automated agents can repeatedly test configurations, simulate attack paths and expose weaknesses before an adversary uses them. The value comes from a feedback loop: findings must be fixed, verified and tracked rather than merely reported.
Architecture and defense in depth
Longer-term gains may come from better software design, stronger isolation, safer defaults and recovery plans that AI helps organizations model and maintain. These improvements are organizational projects, not an instant capability obtained by adding a model to a security console.
What did the controlled attack-versus-defense experiment find?
A preliminary preprint by Balassone and colleagues, posted October 20, 2025, compared agentic systems in 23 Linux Attack/Defense CTF battlegrounds. In the study’s unconstrained scoring, defensive agents achieved a reported 54.3% patching success, while offensive agents achieved 28.3% initial-access success; the reported difference was statistically significant at p=0.0193. Those percentages describe that experiment, not breach rates or enterprise effectiveness.
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| Study condition | Defensive result | Offensive result | What it means |
|---|---|---|---|
| Unconstrained task scoring | 54.3% patching success | 28.3% initial-access success | Defense had the higher measured task score in this setting. |
| Availability and intrusion-prevention constraints | Success fell | Compared under the stricter operational rules | The authors found no significant difference between roles. |
The experiment used one language model, Linux systems, a small sample with many draws and a CTF environment. CTFs provide repeatable conditions, but they omit much of an organization’s identity architecture, change control, business dependencies, cloud complexity and human behavior. The result is useful evidence that scoring rules alter the apparent balance, not a prediction of how often real attackers or defenders will succeed.
Read the full preprint at “Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs.”
Why do defensive success criteria change the result?
Defense is not simply the act of changing a vulnerable file. A real service may need to stay available, preserve data, maintain performance and block an attacker while the fix is tested and deployed. When a benchmark counts only unconstrained patching, it can make defense look stronger than it is in an operating environment.
The CTF comparison illustrates this problem: once availability and intrusion-prevention requirements were imposed, defensive performance declined and the study no longer found a significant role difference. A fair evaluation should therefore score at least three outcomes separately:
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- Intrusion prevention: Did the fix stop the attack path rather than merely alter a file?
- Availability: Did the protected service continue to meet its operational requirements?
Combining these into one percentage can hide trade-offs. For example, an aggressive patch that takes a service offline may block one exploit while creating a different operational failure.
What evidence limits prevent a definitive winner?
Comparisons remain difficult because researchers do not yet share a broadly accepted way to evaluate AI-enabled cyber capabilities over time. The National Academies states: “There are no widely accepted frameworks for evaluating AI-enabled cyber capabilities or tracking their evolution.” That is a qualitative finding about the state of measurement, not a numerical estimate.
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- Model and system version: capability can change after a model update, a tool change or a software patch.
- Data and permissions: a defender with high-quality telemetry may outperform a stronger model that lacks access; an attacker with stolen credentials has a different starting point from one without them.
- Human expertise: agents may need people to set objectives, validate output and handle exceptions.
- Environment: a scripted Linux CTF is more controlled than a heterogeneous enterprise or a safety-critical network.
- Sample size and uncertainty: many draws or few battlegrounds make broad conclusions fragile.
- Objective function: maximizing patch speed, preventing compromise, preserving uptime and recovering safely are different goals.
The NIST Cybersecurity Framework Profile for Artificial Intelligence (IR 8596) was an initial preliminary draft dated December 2025. It discusses defensive opportunities and the need to judge whether capabilities are mature enough for an organization’s needs; it should not be presented as final guidance without checking for a later edition.
Could defenders gain the advantage over the longer term?
Yes, but only if organizations turn AI capability into a continuous defensive system. The National Academies consultation says, “Over the longer term, AI may enable a fundamentally stronger defensive posture.” Its condition is sustained investment in AI-driven defense in depth, better modeling and testing, improved system architecture and coordination.
A long-term defensive shift would require more than purchasing access to a model. Organizations would need reliable asset and identity inventories, usable telemetry, secure automation boundaries, tested recovery, software quality practices and teams able to review machine output. They would also need to measure whether automation reduces exposure without increasing outages or hidden dependencies.
Attackers may receive rapid benefits from general-purpose capabilities, while defenders can compound gains by embedding validated automation into every stage of prevention, detection, response and recovery. That compounding is a possibility, not an established trend; it depends on execution and continued evaluation.
Does current evidence show that AI can run a complete autonomous attack?
No. The sources support AI assistance with individual offensive and defensive tasks, not a universal claim that a system can independently plan and execute a sophisticated campaign against any target. A model may generate reconnaissance notes, code or messages while still needing a human to choose targets, obtain access, adapt to defenses and manage consequences.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an organization test its own AI balance?
Use a controlled, outcome-based evaluation rather than asking whether an AI product is “better” in the abstract. The following process keeps attacker and defender claims comparable.
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- Define the mission and boundary. Choose a service, network segment or application, identify permitted data and tools, and document whether the exercise is a lab, cloud environment or production-like setting.
- Separate attack stages. Score reconnaissance, discovery, exploitation, detection, containment, remediation and recovery independently. Do not let a strong result in one stage stand in for campaign success.
- Set operational constraints before testing. Record uptime, latency, data-integrity and change-control requirements. Include intrusion prevention, not just whether a patch was applied.
- Record access and versions. Capture the model name and version, prompts or policies, tools, permissions, data sources, human interventions and time or compute limits so another team can reproduce the comparison.
- Use matched baselines. Compare AI-assisted staff with the existing process under the same scenario. Report both successful actions and harmful or unusable outputs.
- Measure uncertainty. Repeat scenarios where practical, show draws and failures, and avoid converting a small trial into a general breach probability.
- Verify production safety. Test whether automated changes preserve availability, prevent the intended intrusion and support rollback. A remediation that cannot be safely deployed is an incomplete result.
- Reassess after changes. Re-run the evaluation when models, tools, software, identities or threat assumptions change. Capability can evolve faster than a static control assessment.
This approach follows the comparison axes highlighted by the National Academies, CSET, the CTF study and the international scientific report: task, outcome, access, environment, model version and uncertainty must travel with every claim.
What should leaders and policymakers measure next?
Organizations and researchers need metrics that connect model capability to operational consequences. Useful reporting would distinguish time and cost to complete each attack or defense stage, success under availability constraints, human review requirements, false positives, recovery quality and transfer from test systems to real deployments.
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Does attacking AI systems change this comparison?
It can, but it is a related question rather than the same one. The attacker-versus-defender balance discussed here concerns using AI to attack traditional information systems and to defend them. Attacks on the AI systems themselves—such as manipulating training data, prompts or model interfaces—introduce additional failure modes and controls that require separate analysis.
The international scientific report and NIST’s preliminary profile both emphasize the dual-use nature of advanced AI. An organization should therefore protect AI components as assets while evaluating how those components alter its broader cyber risk.
Frequently Asked Questions
Is an attacker advantage the same in every industry?
No. The balance depends on the sector’s exposure, patching speed, identity controls, regulatory constraints and tolerance for downtime. A well-instrumented cloud service and a legacy industrial environment can produce very different results from the same model.
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Why are CTF results still useful if they do not predict breaches?
A CTF can isolate a task and make competing systems repeatable under the same rules. Its value is diagnostic: it shows which capabilities and scoring assumptions affect performance, while leaving real-world generalization to separate validation.
Can buying an AI security system guarantee a defensive edge?
No. The evidence points to integration, data quality, permissions, human oversight and operational discipline as decisive factors. A model without reliable telemetry or safe deployment controls may add little capability and can create new failure modes.
How often should an organization reassess AI-enabled cyber capabilities?
There is no universally accepted schedule because evaluation frameworks are still developing. Reassess after material changes to models, tools, software, identity permissions or threat conditions, and repeat representative scenarios rather than relying on a one-time score.
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