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Yes, but only in a narrow, qualified sense. AI agents have found bugs, reproduced them, and built working exploits in controlled benchmarks. They have also produced novel findings in simulated smart-contract environments. The evidence does not show that an agent can independently choose arbitrary live targets, discover every weakness, defeat real-world defenses, and cause harm reliably without human setup, authorization, or intervention.
The crucial distinction is between finding a possible vulnerability, proving it is reproducible, and exploiting it to create a concrete security impact. Those are different technical achievements, and a result at one stage does not automatically imply success at the next.
What “without human oversight” means in current tests
In published evaluations, autonomy is task-bounded rather than unrestricted. Researchers select the target, prepare the software and tools, define the objective, constrain the environment, and decide what counts as success. The agent may then inspect code, run commands, generate inputs, debug failures, and iterate without a person approving every step.
That setup demonstrates that an agent can complete a specified security task under specified conditions. It does not demonstrate that the same system can independently select valuable targets, obtain access, understand an unfamiliar production environment, evade monitoring, chain several weaknesses, and operate at scale without human preparation.
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For a useful answer, keep three stages separate:
- Discovery: identifying a suspected flaw in code or observed behavior.
- Validation or reproduction: showing that the flaw can be triggered reliably under defined conditions.
- Exploitation: turning the flaw into a security capability, such as unauthorized access, arbitrary code execution, privilege escalation, or asset theft.
Some benchmarks combine these stages, while others deliberately test only one. Comparing their percentages as if they measured the same ability produces a misleading answer.
What the strongest benchmark results actually show
CVE-Bench: exploitation of critical web vulnerabilities
CVE-Bench, described by its 2025 authors, evaluates agents against critical-severity Common Vulnerabilities and Exposures affecting real-world web applications. The tests run in a sandboxed framework. The strongest reported agent framework exploited up to 13% of the benchmark vulnerabilities.
That figure is a best result for that benchmark’s vulnerability set and evaluation procedure, not a 13% success rate against all software or all attacks. The denominator is the CVE-Bench collection, the targets are prepared web applications, and the environment is isolated. A live service may have different code, authentication, network controls, logging, patches, and data, all of which can change the outcome.
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ExploitGym: extending a trigger into a working exploit
The 2026 ExploitGym preprint uses a different starting point. It covers 898 real-world vulnerability instances across userspace programs, Google’s V8 JavaScript engine, and the Linux kernel. Agents receive an input that triggers the vulnerability and must progressively develop it into a working exploit.
Under the paper’s evaluation settings, one reported configuration produced working exploits for 157 of 898 instances; another named configuration produced 120 of 898. These raw counts show meaningful capability, but they cannot be ranked directly against CVE-Bench’s 13%. ExploitGym gives the agent a vulnerability-triggering input, while a discovery benchmark might require locating the flaw first. The target types, tools, time limits, model versions, and success tests also differ.
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ExploitGym’s results therefore support a specific conclusion: once an agent is pointed toward a real vulnerability, it can sometimes perform the difficult engineering work needed to turn that trigger into an exploit. They do not establish that the same agent can find an equivalent fraction of unknown bugs in arbitrary software.
SCONE-bench: smart contracts in local simulations
SCONE-bench, reported in 2025, evaluates 405 previously exploited smart contracts from Ethereum, Binance Smart Chain, and Base. Runs occur in Docker containers with local blockchain forks. The reported $4.6 million figure is simulated exploit revenue for contracts exploited after the models’ knowledge cutoffs; it is not money taken from real users.
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The authors also tested 2,849 recently deployed contracts without known vulnerabilities in simulation. Agents found two novel vulnerabilities and generated exploits with $3,694 in simulated value. The result is notable because it goes beyond replaying known incidents, but it remains a controlled experiment. The simulated contracts, local forks, available tooling, and scoring rules do not reproduce every condition of a live blockchain, and no real assets were affected.
“We never tested exploits on live blockchains and our work had no impact on real-world assets.”
That qualification from Anthropic’s report is essential. A simulated dollar value is an outcome metric inside the test environment, not evidence of theft or profit in production.
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ExploitBench: why a proof of concept is not arbitrary code execution
ExploitBench explicitly separates vulnerability reproduction from an exploit that reaches a security capability. In its V8 evaluation, the model receives a vulnerable build and the fixing patch. The system then scores whether the generated artifact reaches defined capabilities. Higher tiers use challenge-response tests across randomized heap layouts.
This design addresses a common mistake: a script that crashes a process or demonstrates that a code path is reachable may be a valid proof of concept without giving an attacker arbitrary code execution. Reliable exploitation usually requires additional work, such as controlling memory layout, bypassing mitigations, satisfying an input format, or achieving a capability beyond a denial of service.
How the evaluations differ
The following comparison explains why no single percentage answers whether AI can hack software generally.
| Evaluation | Primary task | Targets and starting information | Environment | Reported result |
|---|---|---|---|---|
| CVE-Bench (2025) | Exploit critical-severity web vulnerabilities | Real-world web applications; benchmark-defined setup | Sandbox | Up to 13% exploited by the strongest reported framework |
| ExploitGym (2026) | Turn a vulnerability-triggering input into a working exploit | 898 instances in userspace software, V8, and Linux; trigger supplied | Controlled benchmark environment | 157/898 for one reported configuration; 120/898 for another |
| SCONE-bench (2025) | Reproduce known smart-contract exploits and discover novel ones | 405 previously exploited contracts; 2,849 recent contracts tested for new findings | Docker containers and local blockchain forks | $4.6 million simulated value on known cases; two novel vulnerabilities and $3,694 simulated value on recent contracts |
| ExploitBench | Reproduce a bug and reach scored security capabilities | V8 vulnerable build and fixing patch; higher tiers add randomized heap layouts | Automated challenge-response evaluation | Capability-based scoring rather than a universal attack rate |
| Codex Security evaluation | Repository-wide detection, validation, and patch proposals | Known and synthetically introduced vulnerabilities in “golden” repositories | Isolated sandbox with human review of proposed fixes | 92% recall, according to the vendor’s 2025 announcement and March 2026 update |
For any comparison, check seven axes: the task stage, target technology, information supplied at the start, tools and time budget, whether the environment is simulated or live, the success criterion, and the model and trial methodology. Raw percentages and simulated dollar totals are not interchangeable.
What defensive AI systems do in practice
OpenAI described a system first called Aardvark and later renamed Codex Security. Its documented workflow scans repositories and commits, investigates potential findings in an isolated sandbox, and attaches proposed patches for human review. The March 2026 update describes the product as a research preview.
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OpenAI reported 92% recall on its “golden” repositories. Those repositories included known vulnerabilities and vulnerabilities introduced synthetically for evaluation. Recall measures how many of the labeled issues the system identified in that test collection; it does not mean that 92% of all vulnerabilities in arbitrary production code will be found, nor that every finding is exploitable.
The human-review step is not cosmetic. A scanner can misclassify intended behavior, misunderstand deployment assumptions, propose an unsafe patch, or miss the business impact of a technically valid flaw. Validation in a sandbox reduces risk while a reviewer decides whether the finding is real, how serious it is, and whether the fix preserves functionality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why unrestricted autonomous exploitation remains unproven
Targets are selected and prepared by people
Benchmarks begin with a defined target and a reproducible environment. An unattended attacker would also need to identify reachable systems, map their versions, obtain a foothold, handle authentication, and decide whether activity is authorized. Those steps are generally outside the reported experiments.
Real defenses change the problem
Production systems add authentication, rate limits, web application firewalls, endpoint controls, sandboxing, memory protections, monitoring, changing dependencies, and unpredictable data. A proof of concept that works in a static container may fail when one assumption changes.
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Exploitation often has to work across randomization, timing variation, restarts, different hardware, and incomplete information. ExploitBench’s randomized heap-layout tests illustrate why a one-off crash or demonstration is weaker evidence than repeatable capability under variation.
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Impact requires more than technical reachability
Even an exploit that executes code may not reach sensitive data or a valuable account. Authorization boundaries, network segmentation, least privilege, transaction limits, and detection can restrict impact. Conversely, a low-level bug may be severe in one deployment and harmless in another.
Results age quickly
The 2025 International AI Safety Report said traditional methods remained dominant for vulnerability discovery at that time. Newer work, including the 2026 ExploitGym preprint, reports stronger agents on specific exploitation tasks. These statements are not contradictory: capability is advancing, benchmarks change, and a snapshot should not be treated as a permanent ranking against human experts.
What this means for security teams
Use agents as force multipliers, not autonomous owners
Agents can help triage large codebases, generate test cases, explain data flows, reproduce reports, and explore exploit hypotheses. Keep a person accountable for authorization, severity, disclosure, remediation, and production changes.
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- Use software you own or are explicitly authorized to test.
- Run agents in disposable containers, virtual machines, or isolated cyber ranges.
- Remove production credentials, personal data, signing keys, and real funds.
- Use local blockchain forks or synthetic services for smart-contract work.
- Log commands, files changed, network access, model version, and time limits.
- Block outbound traffic by default and require an explicit approval path for any exception.
Require reproducible evidence
A useful finding should include the affected version, exact input or request, prerequisites, expected and observed behavior, and a minimal reproduction. For an exploit claim, record the capability achieved, reliability across repeated runs, mitigations bypassed, and the boundary of what was not demonstrated.
Review patches as carefully as findings
Automated fixes can remove a symptom while leaving a variant, introduce authorization gaps, or break legitimate behavior. Test the patch with regression suites, security tests, and deployment-specific checks before release. Treat a proposed patch as code requiring review, not as proof that the vulnerability has been solved.
A practical way to interpret a new AI-security claim
- Identify the stage: Is the claim about discovery, reproduction, or a concrete exploit capability?
- Read the starting conditions: Did the agent receive source code, a crash, a triggering input, a fixing patch, documentation, or credentials?
- Inspect the target: Is it a web app, native program, browser engine, kernel, or smart contract?
- Check the environment: Was the test simulated, sandboxed, containerized, or live?
- Check the denominator: How many cases were attempted, and how many were successful?
- Check repeatability: Did the result survive randomized layouts, restarts, or independent trials?
- Separate vendor claims: Metrics from a product publisher, such as the 92% recall figure, should be labeled as vendor-reported and compared with independent evaluations cautiously.
- Define the impact: A crash, code execution, account takeover, and asset theft are different outcomes.
Bottom line for the question
AI can already find some vulnerabilities and, in bounded evaluations, produce working exploits without a human directing every intermediate action. The clearest evidence includes CVE-Bench’s up-to-13% result on sandboxed critical web vulnerabilities, ExploitGym’s 157 successful exploits out of 898 instances in one configuration, and SCONE-bench’s two novel smart-contract findings in simulation.
Those results establish task-specific capability, not reliable unattended hacking of arbitrary live systems. Human oversight remains necessary for target selection, authorization, containment, interpretation of impact, responsible disclosure, and approval of fixes. The defensible current description is therefore: AI is an increasingly capable autonomous component inside controlled security workflows, not a proven hands-off attacker for the open internet.
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