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LLMs can now be tested on more than vulnerability descriptions: they can be asked to turn a known bug into a working exploit that gains an unauthorized capability. Anthropic’s Frontier Red Team evaluations published May 22, 2026, show a wide performance gap between models and a sharp drop-off between triggering a flaw, creating exploit primitives, escaping a sandbox, and obtaining full control. Mythos Preview led the reported trials, but the scores are measurements inside controlled harnesses—not estimates of how often an AI could compromise arbitrary live systems.
What does it mean for an LLM to “develop an exploit”?
Exploit development is the end-to-end process of converting a vulnerability into a repeatable action that violates the target’s security boundary. That is substantially harder than reproducing a crash, demonstrating an out-of-bounds access, or explaining why a patch works. A meaningful evaluation therefore checks whether the model can construct and use exploit primitives, such as controlled reads and writes, and then reach an unauthorized outcome such as arbitrary code execution (ACE).
The distinction matters because a model may correctly identify a bug yet fail at heap manipulation, address discovery, sandbox escape, reliability, or the final control transfer. Measuring those stages separately exposes where capability stops instead of collapsing every partial attempt into a pass or fail.
How does ExploitBench measure exploit capability in V8?
ExploitBench uses 41 patched vulnerabilities in Google’s V8 JavaScript and WebAssembly engine. Each task is checked against a known target, and the benchmark reports the highest verified capability reached rather than treating a crash as equivalent to code execution.
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What are the five ExploitBench tiers?
| Tier | Capability measured | What a pass means |
|---|---|---|
| T5 | Coverage | The model reaches the relevant code path or vulnerability conditions. |
| T4 | Reproduction | It reliably reproduces the vulnerability’s effect on the target. |
| T3 | Target primitives inside the V8 sandbox | It creates useful exploit primitives while remaining inside the sandbox boundary. |
| T2 | Generic primitives crossing the sandbox | Those primitives work beyond the sandbox’s intended restrictions. |
| T1 | Full control | The exploit reaches the benchmark’s highest control objective, including ACE where required. |
Lower tiers are checked with differential execution against patched builds: the harness compares behavior on vulnerable and fixed versions to verify that the claimed effect depends on the intended flaw. Higher tiers use randomized heap layouts and challenge-response checks, making it harder to pass with a brittle or accidental result. Transcript scanning is used as a backstop, not as the sole proof of success.
The published trials use 300 turns. That budget is important: a result means the model reached a capability under that interaction limit, not that it can always do so with unlimited time or attempts.
What did ExploitBench show about model performance?
When the Baseline and Nudged ExploitBench variants are combined, Mythos Preview reached arbitrary code execution on 21 of the 41 CVEs. Anthropic reported it as the only tested model that could reliably escape the V8 sandbox. Those figures describe this set of patched V8 targets and the stated trial procedure; they are not a prevalence rate for vulnerabilities in the wider software ecosystem.
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How does ExploitGym test end-to-end code execution?
ExploitGym broadens the target set to 898 patched vulnerabilities drawn from OSS-Fuzz projects, V8, and the Linux kernel. For each task, the model receives vulnerable source and build information, a compiled target, launch details, and a remote endpoint. It must use the intended vulnerability to obtain unauthorized code execution and retrieve a generated flag.
What controls are used?
- The baseline gives every model the same prompts and a two-hour wall-clock limit.
- The harness can toggle defenses, including the V8 heap sandbox and Linux kernel address-space layout randomization (KASLR).
- Success is tied to code execution through the intended vulnerability and flag retrieval, rather than to a claimed exploit in a transcript.
These controls make ExploitGym a capability measurement under specified conditions. Disabling a mitigation in a baseline run can isolate a skill, but it also means the score should not be read as performance against a fully hardened deployment.
What were the reported ExploitGym results?
| Model | Intended-vulnerability code-execution successes | Run conditions |
|---|---|---|
| Mythos Preview | 157 | Two-hour baseline across the 898-task corpus |
| Opus 4.6 | 15 | Same ExploitGym setup and time limit |
The gap shows why target diversity matters. Browser-runtime tasks, general open-source software, and kernel vulnerabilities demand different debugging, memory, and environment skills. A model’s aggregate score can therefore change substantially with the mix of vulnerability classes, even when the prompt and time budget stay fixed.
What does SCONE-bench measure that the other tests do not?
SCONE-bench evaluates smart-contract exploitation in a local simulation. The updated run contains 12 exploits reported after the models’ January 1, 2026 knowledge cutoff, sourced from DefiHackLabs. This post-cutoff construction helps test whether a model can derive an attack from the contract and incident conditions rather than simply recalling a published exploit.
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For each successful simulated exploit, the framework converts the real incident’s proceeds to US dollars using the historical exchange rate on that incident date, then sums the values. Mythos Preview exploited a reported $35 million in simulated value, which Anthropic says was $15 million, or about 75 percent, more than the next-closest model.
Those dollars are an impact-weighted benchmark score, not money taken from live contracts. Local execution removes operational uncertainty, and historical conversion standardizes the comparison; neither reproduces the legal, monitoring, liquidity, or timing constraints of an actual incident.
How should the three benchmarks be compared?
| Benchmark | Target class | Success definition | Defenses and budget | What the score tells you |
|---|---|---|---|---|
| ExploitBench | V8 JavaScript/WebAssembly engine | Highest verified tier among 16 capabilities in five tiers | Vulnerable and patched builds; randomized heaps and challenge-response at higher tiers; 300 turns | Where exploit construction fails between reproduction, primitives, sandbox escape, and full control |
| ExploitGym | OSS-Fuzz projects, V8, and Linux kernel | Intended-vulnerability ACE plus generated-flag retrieval | Two-hour baseline; V8 sandbox and Linux KASLR configurable | End-to-end performance across a broad, heterogeneous target set |
| SCONE-bench | Smart contracts | Successful simulated exploit, aggregated by historical USD value | Local simulation; 12 post-cutoff incidents; historical exchange rates | Relative ability to chain contract logic into financially weighted outcomes |
The measures are complementary, not interchangeable. ExploitBench supplies a fine-grained capability ladder, ExploitGym tests breadth and completion under a deadline, and SCONE-bench adds a domain where state transitions and economic incentives dominate memory corruption.
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- They use prepared targets. The vulnerabilities are patched and packaged with source, builds, launch information, or simulations. Real environments add asset discovery, configuration drift, authentication barriers, and noisy telemetry.
- Time and interaction limits shape outcomes. A two-hour ExploitGym run or a 300-turn ExploitBench trial measures performance inside that budget. More or fewer turns could change the ranking.
- Mitigations are experimental variables. Toggling the V8 heap sandbox or Linux KASLR isolates specific capabilities, but it does not represent every production defense stack.
- Scoring is harness-specific. Differential execution, challenge-response, transcript checks, flag capture, and simulated revenue reward different behaviors. A model optimized for one harness may not transfer cleanly to another.
- Incident probability is absent. The benchmarks do not estimate how often an attacker would find a comparable target, avoid detection, maintain access, or monetize it.
For these reasons, the safest interpretation is comparative: under the published conditions, one model demonstrated stronger measured exploit-building capability on these tasks. It is not evidence that the model can compromise arbitrary live systems or that the reported success rates are forecasts.
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How can you design a rigorous benchmark for exploit-writing models?
- Define the unauthorized outcome first. Decide whether the endpoint is a crash, a vulnerability-specific primitive, sandbox escape, ACE, data extraction, or another measurable capability. Do not call every reproduction an exploit.
- Use patched and vulnerable references. Differential execution can verify that the behavior depends on the intended bug rather than an unrelated environmental mistake.
- Separate capability stages. Publish intermediate tiers so readers can distinguish trigger reliability from memory control, boundary crossing, and final control.
- Automate proof where possible. Challenge-response checks, randomized layouts, and generated flags reduce dependence on subjective transcript judgments. Keep transcript review as a diagnostic backstop.
- Vary target classes. Include browser engines, ordinary open-source software, kernels, and smart contracts only when each has a clear, domain-appropriate success test.
- Report defenses and budgets beside every number. State the model prompt, turn or wall-clock limit, enabled mitigations, build versions, and whether a judge or deterministic harness decided success.
- Use fresh or post-cutoff cases carefully. Post-cutoff incidents can test construction rather than memorization, but they still need reproducible local environments and transparent provenance.
- Publish failures, not only totals. A tier distribution, intended-versus-unintended vulnerability breakdown, and per-class results show where additional safeguards or training are needed.
What is the practical conclusion for security teams?
LLM exploit capability is real enough to warrant direct measurement, but the measurement must be staged and reproducible. The 2026 evaluations indicate that Mythos Preview performed far better than the comparison models across the reported V8, general-software/kernel, and smart-contract tasks, while also showing that success depends strongly on the benchmark’s target class, defenses, scoring rule, and time budget.
Security teams should treat these results as a reason to test their own exposure: build isolated, authorized harnesses; measure the highest capability a model can reach; and record whether mitigations change the result. They should not convert benchmark counts or simulated dollars into claims about live-system compromise rates.
Frequently Asked Questions
Can a model pass an exploit benchmark without finding the intended bug?
It can appear successful if a benchmark accepts an unintended path, which is why strong harnesses tie success to the intended vulnerability. ExploitGym explicitly requires intended-vulnerability code execution, while differential checks in ExploitBench compare vulnerable and patched builds.
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Cases reported after January 1, 2026 reduce the chance that a model is recalling a known write-up. They still require a local, reproducible setup, so the result measures reconstruction and execution rather than access to a live blockchain.
What information should a benchmark report for each failed attempt?
Useful records include the highest tier reached, whether the trigger was reliable, which mitigation blocked progress, elapsed time or turns, and whether the model used the intended vulnerability. Per-target failure data is more actionable than one aggregate percentage.
How can independent researchers reproduce these kinds of measurements?
They need versioned vulnerable and patched targets, deterministic launch instructions, an isolated execution environment, automated success checks, and fixed prompts and budgets. Anthropic points interested researchers to its External Researcher Access Program for access to additional evaluation work.
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