Yes, but only in bounded conditions demonstrated so far. AI agents have reproduced bugs, found some previously unknown flaws in simulated systems, and built working exploits in controlled benchmarks. Those results do not show that an agent can independently choose any live target, discover every weakness, defeat real defenses, and cause damage without people setting up, supervising, and containing the operation.
The crucial distinction is between finding a suspected vulnerability, proving that it is reproducible, and building an exploit that creates a concrete security impact. A success at one stage does not automatically imply success at the next.
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What “without human oversight” means in current evaluations
In published tests, autonomy is task-bounded. People select the software, define the objective, provide an environment, decide which tools are available, set a time budget, and score the result. An agent may then inspect code, run commands, generate inputs, debug failures, and iterate on its own inside that prepared boundary.
That is meaningful automation, but it is not unrestricted operation. The evidence does not establish that a model can independently select arbitrary internet targets, obtain authorization, maintain access, evade defenders, and deliver reliable impact at scale. Every result below should therefore be read with its target type, starting information, model date, tools, and environment attached.
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What the strongest benchmark results actually show
| Evaluation | Task and target | Starting conditions and environment | Reported result | What the result does not prove |
|---|---|---|---|---|
| CVE-Bench (2025) | Exploit critical-severity real-world web-application vulnerabilities. | Sandboxed benchmark based on Common Vulnerabilities and Exposures; the framework and test set were selected by the benchmark authors. | The strongest agent framework exploited up to 13% of benchmark vulnerabilities. | It is not a 13% success rate against all web applications or all vulnerabilities in the wild. |
| ExploitGym (2026 preprint) | Extend vulnerability-triggering inputs into working exploits for userspace programs, Google’s V8 JavaScript engine, and the Linux kernel. | Agents receive an input that already triggers the vulnerability and must progressively develop an exploit under the paper’s evaluation settings. | Working exploits for 157 of 898 instances in the strongest reported configuration, and 120 of 898 for another named configuration. | The agent is not starting from an unknown target with no clue; the supplied trigger narrows the discovery problem. |
| SCONE-bench (reported 2025) | Exploit smart contracts on Ethereum, Binance Smart Chain, and Base. | Docker containers with local blockchain forks. The benchmark included 405 contracts that had previously been exploited. | A reported $4.6 million in simulated exploit revenue on contracts exploited after the models’ knowledge cutoffs. | No real users lost that money; the figure is simulated value in a local environment. |
| SCONE-bench novel-contract test | Look for vulnerabilities in recently deployed contracts without known vulnerabilities. | 2,849 contracts tested in simulation rather than on live blockchains. | Two novel vulnerabilities and $3,694 in simulated exploit value. | Anthropic states that no live blockchain or real-world asset was touched. |
| ExploitBench | Separate vulnerability reproduction from exploits that reach a security capability, using a V8 setup. | The model receives a vulnerable build and its fixing patch. Higher tiers use challenge-response tests across randomized heap layouts. | Scores whether the agent reaches capabilities such as a concrete security impact, rather than merely producing a plausible proof of concept. | A reproducible trigger or proof of concept is not automatically arbitrary code execution. |
The percentages and counts are not directly comparable. CVE-Bench, ExploitGym and SCONE-bench use different software, inputs, success criteria and containment. A larger simulated dollar figure does not mean a more capable agent than a lower percentage in another benchmark.
Three stages that should never be conflated
Discovery: identifying a possible flaw
Discovery is the act of noticing suspicious behavior, an unsafe data flow, a missing authorization check, a memory error or another condition that might be exploitable. Static analysis, fuzzing, code review and runtime instrumentation can all produce candidates. At this stage, the agent may be wrong, may have found a harmless crash, or may lack enough context to determine impact.
Validation and reproduction: showing the flaw is real
Validation demonstrates that a specific input or sequence reliably reaches the suspected bug under defined conditions. ExploitBench describes proof-of-concept work in this sense: it shows that a bug is reproducible or reachable. Reproduction generally requires less capability than an attack. A crash that can be triggered locally may still be difficult to turn into data disclosure, privilege escalation or code execution.
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Exploitation: causing a security capability
Exploitation turns the flaw into a concrete outcome, such as unauthorized access, arbitrary code execution, an account takeover or theft of digital assets. Exploit evaluations therefore test more than whether an input reaches a vulnerable line. They may require bypassing mitigations, controlling memory or satisfying a challenge-response check. ExploitGym’s agents begin with a vulnerability-triggering input, then attempt the harder progression toward a working exploit.
How much autonomy agents receive
A benchmark run can look autonomous while still depending on substantial human preparation. The following dimensions determine what an agent was really asked to do:
- Target selection: whether researchers chose one known vulnerable component or allowed the agent to search a broad corpus.
- Initial information: whether the model received source code, a binary, a vulnerable build, a patch, a crash, or an input already known to trigger the bug.
- Tools: access to compilers, debuggers, fuzzers, disassemblers, browsers, blockchain nodes, internet services or only a restricted command environment.
- Time and compute: a fixed trial window and resource budget can make a difficult task feasible or impossible.
- Environment: a Docker container, local fork, sandbox or simulation prevents the consequences of a successful run from reaching production systems.
- Scoring: a benchmark may count a crash, a reproducible trigger, a challenge-response success or a demonstrated impact as success; those outcomes have very different meanings.
- Model and trial method: model versions change quickly, and one run or one configuration may not represent average performance.
Evidence for novel vulnerability discovery
The most striking discovery result in the cited material comes from Anthropic’s 2025 smart-contract evaluation. Agents examined 2,849 recently deployed contracts that were not known to contain vulnerabilities and found two novel flaws, producing $3,694 in simulated exploit value. The contracts were tested in a simulation, not on live networks. Anthropic explicitly states: “We never tested exploits on live blockchains and our work had no impact on real-world assets.”
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This demonstrates that agents can sometimes discover beyond a list of previously catalogued bugs when given a structured environment and suitable tooling. It does not establish a general rate for finding unknown vulnerabilities in arbitrary applications. Two findings in one simulated corpus cannot be extrapolated to all smart contracts, and simulated value is not equivalent to money that could be taken from users.
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OpenAI describes Aardvark, later renamed Codex Security, as a defensive workflow that analyzes repositories, scans commits, validates potential findings in an isolated sandbox and attaches proposed patches for human review. Its March 2026 update reports 92% recall on “golden” repositories containing known and synthetically introduced vulnerabilities. That figure is a vendor benchmark claim, not an independent measurement of performance across every codebase.
The workflow matters because finding a suspicious line is not enough for a safe fix. Validation can reduce false positives, while a proposed patch gives engineers something concrete to inspect. Human review remains responsible for deciding whether the finding is valid, whether the patch preserves intended behavior and whether deployment is safe.
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Why live, unattended exploitation remains unproven
Real systems add complications that controlled benchmarks often remove:
- Asset owners may not authorize testing, and identifying a legal target is itself a human governance decision.
- Production systems change during an operation, creating race conditions and unexpected side effects.
- Authentication, rate limits, monitoring, network segmentation and endpoint defenses interact in ways that a prepared sandbox may not model.
- Exploit reliability can depend on hardware, timing, configuration, user data and obscure version differences.
- An agent that reaches a technical capability still needs a plan for persistence, lateral movement or monetization, each of which introduces additional uncertainty and risk.
- Errors have real consequences: a malformed payload can corrupt data, interrupt service or expose confidential information even when the agent’s objective is defensive.
The 2025 International AI Safety Report said traditional methods remained dominant for vulnerability discovery at that time. The 2026 ExploitGym preprint reports stronger results on specific exploitation tasks. These statements are not contradictory: they are snapshots taken with different methods and dates. Rapid model changes make broad, permanent rankings unreliable.
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“Up to 13%” on CVE-Bench
The denominator is CVE-Bench’s sandboxed set of critical-severity web-application vulnerabilities, and the number is the best result reported by its authors for the strongest tested agent framework. It is an upper result for that benchmark configuration, not an average across all systems and not a forecast of attacks against the public internet.
“157 of 898” and “120 of 898” in ExploitGym
These are raw counts from 898 real-world vulnerability instances spanning userspace software, V8 and the Linux kernel. Because each agent starts with an input that triggers the vulnerability, the task focuses on progressing from reachability to exploitation. The counts show that exploitation remains difficult while also showing that defenses and mitigations do not eliminate every measured success.
Simulated dollars in SCONE-bench
The $4.6 million and $3,694 figures represent simulated exploit revenue on local blockchain forks. They help quantify the potential impact of a successful path inside the benchmark, but they are not losses, theft or transfers involving real users. The test’s two novel vulnerabilities are evidence of capability in that simulation, not proof of unattended attacks on live chains.
A safe way to use vulnerability-finding agents
Organizations can obtain useful automation without treating an agent as an unsupervised attacker. Use this sequence for authorized work:
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- Clone or stage the target. Prefer a disposable environment with synthetic secrets, representative configuration and no route to production data.
- Limit capabilities. Use least-privilege credentials, network egress controls, filesystem boundaries and explicit command allowlists. Log every tool call and generated artifact.
- Separate discovery from exploitation. Begin with static analysis, tests and non-destructive reproduction. Require a human decision before any action that could alter data, access another account or send traffic outside the lab.
- Reproduce independently. Have an engineer verify the trigger, affected versions, preconditions and impact. Treat an agent-generated proof of concept as a lead until it passes this check.
- Review patches and regressions. Inspect the proposed fix, run unit and integration tests, and check that the change does not weaken authentication, authorization or input validation elsewhere.
- Disclose responsibly. For third-party software, follow the owner’s security contact and coordinated-disclosure process. Do not publish weaponized details while users remain exposed.
Common failure modes and practical responses
| Failure mode | Why it happens | Response |
|---|---|---|
| False positive reported as a vulnerability | The model infers an unsafe path without proving reachability or impact. | Require a minimal reproducer, affected version and independently observed consequence. |
| Crash mistaken for code execution | A trigger reaches a fault but does not provide control over execution. | Use capability-based checks, such as ExploitBench-style challenge responses, rather than relying on a crash log. |
| Benchmark score overgeneralized | Different benchmarks supply different clues, tools and targets. | Compare only evaluations with matching task, environment, denominator and model date. |
| Unsafe side effect in testing | The agent has production credentials or unrestricted network access. | Use isolated containers or local forks, synthetic data, egress blocks and automatic shutdown limits. |
| Patch introduces a new bug | The generated change fixes one path while altering assumptions elsewhere. | Require human code review, regression tests, security tests and staged deployment. |
| Stale conclusion | Models, tools and defenses improve rapidly. | Record model version and date, rerun representative tests and avoid treating an old score as a permanent capability boundary. |
What organizations should conclude now
AI agents are already useful for narrowing vulnerability queues, generating test cases, reproducing selected bugs and, in controlled settings, producing working exploits. Their strongest demonstrations come from bounded tasks with carefully chosen targets and measurable success conditions. The same evidence does not support handing an agent unrestricted authority over live infrastructure.
For defenders, the practical implication is to assume that automation will increase the speed and volume of both vulnerability discovery and exploit development. Invest in secure defaults, rapid patching, asset inventory, logging and containment, while using agents inside authorized sandboxes with human approval gates. Measure your own systems with realistic, repeatable tests rather than borrowing a percentage from an unrelated benchmark.
So, can AI find and exploit software vulnerabilities without human oversight? It can complete parts of that chain autonomously in controlled evaluations. Reliable, general-purpose, unattended exploitation of arbitrary live systems has not been demonstrated by the cited evidence.
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