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We argued that controlled, task-specific setups do not capture the behaviors that actually matter in the wild, such as exploration, prioritization, strategy, and reliable validation of findings. To address this, we presented our evaluation methodology, centered on realistic targets, a structured finding-to-ground-truth pipeline with LLM-as-a-judge matching and bipartite resolution, continuous expert maintenance of ground-truth, and richer evaluation metrics that go beyond simple success rates.",[795,799],{"_key":779,"_type":796,"href":797,"to":798},"link","https://ethiack.com/info-hub/research/evaluating-pentesting-agents-part-1",{"_key":779,"_type":11,"href":797,"type":11},{"_key":788,"_type":796,"href":800,"to":801},"https://github.com/ethiack/ethibench",{"_key":788,"_type":11,"href":800,"type":11},{"_key":803,"_type":23,"children":804,"markDefs":809,"style":32},"faf850478c1e",[805],{"_key":806,"_type":27,"marks":807,"text":808},"66b26c33bee3",[],"",[],{"_key":811,"_type":23,"children":812,"markDefs":817,"style":32},"997bf9acd61f",[813],{"_key":814,"_type":27,"marks":815,"text":816},"d811ea69996b",[],"However, a complete and robust evaluation protocol for AI Pentesting Agents must be more than just a way to produce performance metrics. In this second part of the series, we address four additional challenges that we strongly believe any real-world-oriented evaluation methodology should cover:",[],{"_key":819,"_type":23,"children":820,"markDefs":824,"style":32},"95933b7839c4",[821],{"_key":822,"_type":27,"marks":823,"text":808},"40a8ec06d709",[],[],{"_key":826,"_type":23,"children":827,"level":469,"listItem":832,"markDefs":833,"style":32},"6664e66f8ca6",[828],{"_key":829,"_type":27,"marks":830,"text":831},"5b9d1cbe808a",[],"LLM-based systems are inherently stochastic and costly to run. This means we need to think carefully about statistical significance under realistic compute and budget constraints.","bullet",[],{"_key":835,"_type":23,"children":836,"level":469,"listItem":832,"markDefs":841,"style":32},"ea3de5cf30b9",[837],{"_key":838,"_type":27,"marks":839,"text":840},"34d096518efe",[],"The stochastic nature of Pentesting Agents can also be useful: when applied multiple times to the same target, it may increase detection rates. To measure that, we need a reliable way to evaluate cumulative agent behavior.",[],{"_key":843,"_type":23,"children":844,"level":469,"listItem":832,"markDefs":849,"style":32},"5caec41e7d26",[845],{"_key":846,"_type":27,"marks":847,"text":848},"d1c045e23939",[],"Finding vulnerabilities is a temporal detection problem, since it depends on observations gathered over time. Tracking detections throughout a run can reveal meaningful patterns, such as diminishing returns or anomalous behavior.",[],{"_key":851,"_type":23,"children":852,"level":469,"listItem":832,"markDefs":857,"style":32},"ac189e1426b5",[853],{"_key":854,"_type":27,"marks":855,"text":856},"eb410f964895",[],"Evaluating AI Pentesting Agents on a broad and realistic target suite with repeated runs is often too expensive to do frequently. As a result, it is often necessary to define smaller target subsets that preserve, as much as possible, the conclusions we would obtain on the full suite while reducing evaluation cost.",[],{"_key":859,"_type":23,"children":860,"markDefs":864,"style":32},"bea94d604b56",[861],{"_key":862,"_type":27,"marks":863,"text":808},"637255a94936",[],[],{"_key":866,"_type":23,"children":867,"markDefs":872,"style":873},"60d1c36b13f3",[868],{"_key":869,"_type":27,"marks":870,"text":871},"c3bc36751666",[],"Dealing with stochasticity under computational constraints",[],"h2",{"_key":875,"_type":23,"children":876,"markDefs":881,"style":32},"6fa64793b037",[877],{"_key":878,"_type":27,"marks":879,"text":880},"8fb3c3e094b2",[],"LLMs are inherently probabilistic, meaning the same prompt can produce different responses on different runs. This variability becomes even greater in agentic systems, where a task may involve many rounds of reasoning, exploration, and tool use. In end-to-end pentesting, small differences can compound into very different outcomes. As a result, a single run is not enough to reliably measure an agent's performance.",[],{"_key":883,"_type":23,"children":884,"markDefs":889,"style":32},"a6706a52f68a",[885],{"_key":886,"_type":27,"marks":887,"text":888},"6f63ad9ee9b2",[],"This challenge is not unique to Pentesting Agents. Many scientific experiments deal with random variation, and statistics provide well-established ways to account for it. When comparing two agent versions, we use Welch's t-test to estimate whether the observed difference is likely to be real or simply due to chance.",[],{"_key":891,"_type":23,"children":892,"markDefs":897,"style":32},"62ac232ffb70",[893],{"_key":894,"_type":27,"marks":895,"text":896},"b63cb0846582",[],"In practice, however, running enough experiments for strong statistical confidence is often too expensive. Evaluating modern agents across realistic targets requires significant compute and cost, so comparisons are frequently based on only a small number of runs. In these cases, a non-significant p-value may simply reflect limited data rather than the absence of a meaningful difference.",[],{"_key":899,"_type":23,"children":900,"markDefs":905,"style":32},"806a2f930dc2",[901],{"_key":902,"_type":27,"marks":903,"text":904},"838f720568bc",[],"For this reason, we also report Cohen's d, an effect size that measures how large the performance difference is relative to the normal run-to-run variation. While the p-value tells us how confident we can be that a difference exists, Cohen's d tells us how meaningful that difference is. Together, these metrics provide a more reliable picture of agent performance, helping distinguish ordinary randomness from genuine improvements.",[],{"_key":907,"_type":23,"children":908,"markDefs":926,"style":32},"737080d398a5",[909,913,918,922],{"_key":910,"_type":27,"marks":911,"text":912},"b8fb8d5c4773",[],"The following table compiles various examples extracted from our experiments (see ",{"_key":914,"_type":27,"marks":915,"text":917},"cfbeb151da99",[916,29],"cafeb753522c","Part 1",{"_key":919,"_type":27,"marks":920,"text":921},"a4f8271d774d",[29]," ",{"_key":923,"_type":27,"marks":924,"text":925},"b660999ae007",[],"for the description of the experiments). The first two comparisons, where Hackian’s Pentesting Agent is compared with the two next ranked agentic setups, are examples where performance differences are confirmed from both a statistical significance (>0.05 threshold) and effect-size (>1.0 threshold) perspective. On the other hand, the remaining cases are examples where by the usual standards we cannot say that the performance difference is statistically significant, but the effect-size measure clearly shows a relevant distinction between the agentic setups.\n",[927],{"_key":916,"_type":796,"href":797,"to":928},{"_key":916,"_type":11,"href":797,"type":11},{"_key":930,"_type":931,"rows":932},"eb943f13ed6b","table",[933,940,944,951,958,965,969,975,981,987,991,996,1002,1008,1012,1018,1024,1030,1034,1039,1045,1051,1055,1061,1067],{"_key":934,"_type":935,"cells":936},"233d4faa-3e5e-4494-ac6a-42b1af00b363","tableRow",[808,937,938,939],"F1","Recall","Precision",{"_key":941,"_type":935,"cells":942},"40ca18fb-c374-451b-87d4-31542b4402e0",[943,808,808,808],"Hackian vs Claude Code Sonnet 4.6",{"_key":945,"_type":935,"cells":946},"69241be7-798e-46b5-8b8f-f40a0558d5ab",[947,948,949,950],"Difference","+12.28%","+12.35%","+9.56%",{"_key":952,"_type":935,"cells":953},"6668e5a9-e6c5-42db-abd1-c2663b94bbf3",[954,955,956,957],"p-value","0.0206","0.0256","0.0982",{"_key":959,"_type":935,"cells":960},"fb6f9ee5-c720-4405-83c5-8e6d424ea95a",[961,962,963,964],"Cohen's d","3.048","3.046","1.970",{"_key":966,"_type":935,"cells":967},"6e158b80-c97a-496b-b5eb-6340c30198c9",[968,808,808,808],"Hackian vs PentAGI Sonnet 4.6",{"_key":970,"_type":935,"cells":971},"e3838678-5ce7-4fab-912c-a9913a42923f",[947,972,973,974],"+16.72%","+13.89%","+21.07%",{"_key":976,"_type":935,"cells":977},"9eb460b4-66f2-43c0-8303-ac8380d2c14c",[954,978,979,980],"0.0218","0.0262","0.0379",{"_key":982,"_type":935,"cells":983},"a9aa3a37-a264-4505-9d48-7f8e14dd75ca",[961,984,985,986],"3.297","2.818","3.319",{"_key":988,"_type":935,"cells":989},"a36fbf3b-4206-4c52-ac1f-23416593cd16",[990,808,808,808],"PentaAGI Sonnet 4.6 vs Strix Sonnet 4.6",{"_key":992,"_type":935,"cells":993},"283f12f2-0ba1-4118-89f1-419e8bec090b",[947,949,994,995],"+17.28%","-25.70%",{"_key":997,"_type":935,"cells":998},"13f4673f-9f58-4c7a-8696-3bf6abb343e3",[954,999,1000,1001],"0.0577","0.0116","0.0276",{"_key":1003,"_type":935,"cells":1004},"5f05ba2f-2585-4a30-8d97-293d86d75c49",[961,1005,1006,1007],"2.168","3.673","-4.147",{"_key":1009,"_type":935,"cells":1010},"9083f354-66aa-4103-b80c-84bca834993e",[1011,808,808,808],"PentAGI Qwen 3.6 Plus vs Strix Qwen 3.6 Plus",{"_key":1013,"_type":935,"cells":1014},"4028ad1c-b3d8-4fb8-bbc4-2e64323401b2",[947,1015,1016,1017],"+11.51%","+10.49%","-26.84%",{"_key":1019,"_type":935,"cells":1020},"a296de7a-ec3b-4fe3-96da-5dfe62ef111e",[954,1021,1022,1023],"0.0756","0.0290","0.0008",{"_key":1025,"_type":935,"cells":1026},"e0edd687-2d4d-484c-9b7a-1ee95309ce91",[961,1027,1028,1029],"2.349","3.066","-9.504",{"_key":1031,"_type":935,"cells":1032},"56b492e2-7f54-478e-a737-38fa9c7afd2b",[1033,808,808,808],"PentAGI Sonnet 4.6 vs PentAGI GPT 5.4",{"_key":1035,"_type":935,"cells":1036},"61eead63-5e34-463e-8f66-d3e81ce43c68",[947,1037,973,1038],"+10.05%","-12.28%",{"_key":1040,"_type":935,"cells":1041},"82a3366f-ba9d-46ba-ba60-902500103852",[954,1042,1043,1044],"0.0835","0.0225","0.1261",{"_key":1046,"_type":935,"cells":1047},"711ebb64-56ba-4ca0-93ec-7cf7e60d437f",[961,1048,1049,1050],"2.033","3.248","-1.591",{"_key":1052,"_type":935,"cells":1053},"6c7851fb-a1bd-4bf0-b50f-75ec6d708e28",[1054,808,808,808],"PentAGI GPT 5.4 vs Strix GPT 5.4",{"_key":1056,"_type":935,"cells":1057},"f23b2516-836d-4d61-826a-4d780db9323d",[947,1058,1059,1060],"+5.75%","+6.17%","-13.15%",{"_key":1062,"_type":935,"cells":1063},"9bca9f28-6372-4263-a71a-43bc894c176c",[954,1064,1065,1066],"0.0948","0.0617","0.0738",{"_key":1068,"_type":935,"cells":1069},"a4eb50e0-4f0c-4ae0-a324-fcd7c86d5371",[961,1070,1071,1072],"1.796","2.182","-2.629",{"_key":1074,"_type":23,"children":1075,"markDefs":1080,"style":873},"21ead3f1578c",[1076],{"_key":1077,"_type":27,"marks":1078,"text":1079},"18e277316830",[],"The importance of cumulative evaluation",[],{"_key":1082,"_type":23,"children":1083,"markDefs":1088,"style":32},"14fffb5434c4",[1084],{"_key":1085,"_type":27,"marks":1086,"text":1087},"75f22b891be1",[],"We already discussed how the stochastic nature of agentic pentesting systems creates challenges, especially when comparing different agent variants. But those same probabilistic properties can also work in our favor.",[],{"_key":1090,"_type":23,"children":1091,"markDefs":1095,"style":32},"6242099beba4",[1092],{"_key":1093,"_type":27,"marks":1094,"text":808},"6c36ac2c79e3",[],[],{"_key":1097,"_type":23,"children":1098,"markDefs":1103,"style":32},"11c5a512df14",[1099],{"_key":1100,"_type":27,"marks":1101,"text":1102},"3a6d0e0a2707",[],"With a fully deterministic system, doing multiple runs on the same exact target is essentially pointless, since the result will be identical every time. With a stochastic system however, run-to-run variation means that even the same Pentesting Agent may uncover different issues in different attempts. In practice, this means we can take advantage of stochastic behavior to discover vulnerabilities that were missed earlier. It also better reflects real-world pentesting, where value often comes from repeated assessment over time rather than from a single execution.",[],{"_key":1105,"_type":23,"children":1106,"markDefs":1110,"style":32},"b0bd4e331b0c",[1107],{"_key":1108,"_type":27,"marks":1109,"text":808},"808acc3314b8",[],[],{"_key":1112,"_type":23,"children":1113,"markDefs":1118,"style":32},"8069265c03d2",[1114],{"_key":1115,"_type":27,"marks":1116,"text":1117},"f49f02d4b800",[],"For this reason, evaluation should report not only per-run metrics, but also cumulative metrics over k runs on the same target. This can be done by merging the agent’s findings, matching and deduplicating them through our finding-to-ground-truth pipeline, and then scoring them jointly. This cumulative view also opens the door to additional experimental settings. For example, repeated runs on the same target can be evaluated without cross-run memory, to isolate the benefit of stochastic diversity, or with memory from previous runs, to assess whether retained information improves efficiency and coverage. It also enables evaluation under target evolution, where the environment changes between runs.",[],{"_key":1120,"_type":23,"children":1121,"markDefs":1125,"style":32},"d9106a834275",[1122],{"_key":1123,"_type":27,"marks":1124,"text":808},"00a7ef275c1d",[],[],{"_key":1127,"_type":23,"children":1128,"markDefs":1133,"style":32},"7dc331e5b067",[1129],{"_key":1130,"_type":27,"marks":1131,"text":1132},"c8bd93197d7b",[],"A robust cumulative evaluation allows us to distinguish agents that repeatedly recover the same issues from those whose variability produces a broader and more useful set of accumulated findings.",[],{"_key":1135,"_type":23,"children":1136,"markDefs":1140,"style":32},"509910a840ed",[1137],{"_key":1138,"_type":27,"marks":1139,"text":808},"be7ab2dbbe44",[],[],{"_key":1142,"_type":23,"children":1143,"markDefs":1156,"style":32},"6a10bf9589ae",[1144,1148,1152],{"_key":1145,"_type":27,"marks":1146,"text":1147},"6832ad02088e",[],"In the following figure, we compare cumulative performance with the averaged performance reported in ",{"_key":1149,"_type":27,"marks":1150,"text":917},"e0e9af0250e3",[1151,29],"b704200b32c3",{"_key":1153,"_type":27,"marks":1154,"text":1155},"c9b0d9df3fd7",[],". To do this, we aggregate the findings from the same three independent runs and pass them through our finding-to-ground-truth pipeline. The result is a clear improvement in F1 across all agentic setups and, in some cases, even changes in the relative ranking seen under averaged results. More importantly, cumulative evaluation reveals meaningful behavioral differences between setups. While the gains in F1 are mostly driven by improvements in Recall, the size and nature of those gains vary considerably. At the same time, aggregating multiple runs can also accumulate false positives, leading to sharp drops in Precision for some setups and making them far less useful for repeated assessments.",[1157],{"_key":1151,"_type":796,"href":797,"to":1158},{"_key":1151,"_type":11,"href":797,"type":11},{"_key":1160,"_type":1161,"asset":1162,"image":1165},"996901022861","imageAsset",{"_ref":1163,"_type":1164},"image-9c821bf63bd09302783e369995e87217aaa99e9b-3525x2541-png","reference",{"alt":9,"extension":87,"height":1166,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1160,"url":1167,"width":1168},2541,"https://cdn.sanity.io/images/ah5kp358/production/9c821bf63bd09302783e369995e87217aaa99e9b-3525x2541.png",3525,{"_key":1170,"_type":23,"children":1171,"markDefs":1184,"style":32},"2ef9f0c375cd",[1172,1176,1180],{"_key":1173,"_type":27,"marks":1174,"text":1175},"78e35ffbcd89",[],"\nIn the next figure, we look at a concrete example from one of the evaluated targets (",{"_key":1177,"_type":27,"marks":1178,"text":1179},"8365e6c40cab",[29],"blog",{"_key":1181,"_type":27,"marks":1182,"text":1183},"8a9f6627085b",[],") to show the difference between average and cumulative performance. We focus on two agentic setups with very different cumulative behavior. PentAGI Sonnet 4.6 sees its F1 score decrease, because the gains in Recall are not enough to compensate for the substantial loss in Precision. In contrast, Strix Sonnet 4.6 shows a strong F1 increase when results are aggregated, driven by large Recall gains with no drop in Precision. This comparison does not establish an overall ranking between the two setups, but it clearly shows that cumulative evaluation can expose meaningful behavioral differences between agentic systems.",[],{"_key":1186,"_type":1161,"asset":1187,"image":1189},"1fb6c2422352",{"_ref":1188,"_type":1164},"image-4516df51e731e55df896d4e45ef74844f54b290d-2656x1248-png",{"alt":9,"extension":87,"height":1190,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1186,"url":1191,"width":1192},1248,"https://cdn.sanity.io/images/ah5kp358/production/4516df51e731e55df896d4e45ef74844f54b290d-2656x1248.png",2656,{"_key":1194,"_type":23,"children":1195,"markDefs":1200,"style":32},"59e467d38bfc",[1196],{"_key":1197,"_type":27,"marks":1198,"text":1199},"ef4f29422131",[],"\nThe following plots make these behavioral differences even clearer. As shown, 75% of the true positive detections produced by PentAGI Sonnet 4.6 are shared across all three runs, in sharp contrast to the much smaller overlap observed with Strix Sonnet 4.6. Even though both setups use the same LLM backbone, their effective level of stochasticity is noticeably different.",[],{"_key":1202,"_type":1161,"asset":1203,"image":1205},"776cc74234ce",{"_ref":1204,"_type":1164},"image-41a5dcde9f135565ada013ae1a904d54f4941123-2656x1249-png",{"alt":9,"extension":87,"height":1206,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1202,"url":1207,"width":1192},1249,"https://cdn.sanity.io/images/ah5kp358/production/41a5dcde9f135565ada013ae1a904d54f4941123-2656x1249.png",{"_key":1209,"_type":23,"children":1210,"markDefs":1215,"style":873},"fc9b6b42a792",[1211],{"_key":1212,"_type":27,"marks":1213,"text":1214},"1466e1f05325",[],"\nTemporal evaluation",[],{"_key":1217,"_type":23,"children":1218,"markDefs":1223,"style":32},"49f6fe174174",[1219],{"_key":1220,"_type":27,"marks":1221,"text":1222},"3648ca7eda47",[],"The temporal dimension of pentesting is often ignored, with most attention placed only on the findings produced by the end of the run. But temporal analysis can give a much finer-grained view of agent behavior by showing how, for example, true positives, false positives, severity, and CWE coverage evolve over time. This makes it possible to identify points of diminishing return, as well as unwanted behaviors such as unusual spikes in false positives.",[],{"_key":1225,"_type":23,"children":1226,"markDefs":1230,"style":32},"245567484d9e",[1227],{"_key":1228,"_type":27,"marks":1229,"text":808},"5fe6f7eaef5b",[],[],{"_key":1232,"_type":23,"children":1233,"markDefs":1246,"style":32},"c370e166c722",[1234,1238,1242],{"_key":1235,"_type":27,"marks":1236,"text":1237},"e426fff2ca35",[],"The following example shows the behavior of Claude Code Sonnet 4.6 on the ",{"_key":1239,"_type":27,"marks":1240,"text":1241},"558f1aa352b6",[29],"paygoat",{"_key":1243,"_type":27,"marks":1244,"text":1245},"bacb41c611fc",[]," target. Across three independent runs, a consistent pattern emerges: false positives begin to accumulate rapidly in the latter half of the runtime, suggesting a drop in validation quality as the agent progresses. This kind of signal is difficult to capture with standard end-of-run metrics, but becomes immediately visible through temporal evaluation, making it easier to pinpoint when and how a Pentesting Agent behavior starts to degrade.",[],{"_key":1248,"_type":1161,"asset":1249,"image":1251},"e35b3b3b5f4d",{"_ref":1250,"_type":1164},"image-ce5374e980fbf92d124369b6e8292c3bcd9955f4-1763x1248-png",{"alt":9,"extension":87,"height":1190,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1248,"url":1252,"width":1253},"https://cdn.sanity.io/images/ah5kp358/production/ce5374e980fbf92d124369b6e8292c3bcd9955f4-1763x1248.png",1763,{"_key":1255,"_type":23,"children":1256,"markDefs":1261,"style":873},"889b9ff4d87d",[1257],{"_key":1258,"_type":27,"marks":1259,"text":1260},"5dce27aa29fc",[],"\nSelecting representative subsets for sustainable experimentation",[],{"_key":1263,"_type":23,"children":1264,"markDefs":1269,"style":32},"932f737a8dcb",[1265],{"_key":1266,"_type":27,"marks":1267,"text":1268},"b8a6c81da919",[],"Evaluating AI Pentesting Agents on a broad and realistic set of targets with repeated runs is often too expensive to do frequently. During iterative feature development, ablation studies, parameter tuning, or small system changes, it is useful to define smaller target subsets that preserve, as much as possible, the conclusions we would obtain on the full benchmark while reducing evaluation cost.",[],{"_key":1271,"_type":23,"children":1272,"markDefs":1277,"style":32},"a76dc1037ad1",[1273],{"_key":1274,"_type":27,"marks":1275,"text":1276},"1de63f8c19c5",[],"One practical way to build smaller evaluation suites is to use data from past benchmark runs. Imagine a benchmark with a large set of targets, each with an associated average monetary evaluation cost. Using historical results from previous evaluations, we calculate the F1 score achieved on the full benchmark, as well as the F1 score that would have been obtained on every possible subset of targets. This allows us to directly measure the trade-off between evaluation cost and fidelity to the full benchmark. From this space of possibilities, we can then select subsets that satisfy a maximum cost constraint, such as keeping evaluation costs below a given fraction of the full benchmark, or subsets that satisfy a minimum correlation threshold, such as maintaining a desired level of agreement with full-benchmark results.",[],{"_key":1279,"_type":23,"children":1280,"markDefs":1285,"style":32},"680390126e7a",[1281],{"_key":1282,"_type":27,"marks":1283,"text":1284},"59d52a173162",[],"Agreement is measured primarily using Pearson correlation and secondarily using Spearman correlation. Pearson correlation is the main selection criterion because our goal is not only to preserve the ranking of systems, but also the magnitude of the performance gaps between them. If one system clearly outperforms another on the full benchmark, a good reduced suite should reflect a similar gap. Spearman correlation is reported as a complementary metric because it focuses on rank ordering and is less sensitive to outliers or nonlinear scaling effects.",[],{"_key":1287,"_type":23,"children":1288,"markDefs":1293,"style":32},"70a23b4f666d",[1289],{"_key":1290,"_type":27,"marks":1291,"text":1292},"21ee1880148c",[],"This gives us a data-driven approach for subset selection. Instead of relying only on judgments about which targets seem difficult, representative, or realistic, the reduced suite is chosen based on its demonstrated ability to reproduce full-benchmark results. At the same time, these subsets should be seen as efficient proxies for routine evaluation, not permanent replacements for full benchmark runs. Since the selection process depends on historical performance data, subsets should be periodically revalidated as both agents and benchmark targets evolve.",[],{"_key":1295,"_type":23,"children":1296,"markDefs":1301,"style":32},"1b37aa1c44f8",[1297],{"_key":1298,"_type":27,"marks":1299,"text":1300},"3f77e0e2597e",[],"The following plot illustrates this trade-off using historical data from runs of Hackian’s Pentesting Agent across multiple targets. For different cost ratios relative to the full benchmark, it shows the best achievable correlation at each budget level. In this case, operating at roughly 40% of the total cost still allows us to select a subset that retains over 80% correlation with the full benchmark, under both Pearson and Spearman metrics. In practice, this provides a strong and efficient proxy for routine experimentation or large-scale parameter tuning, while preserving most of the insights we would expect from a significantly more expensive full evaluation.",[],{"_key":1303,"_type":1161,"asset":1304,"image":1306},"4cd685a7816c",{"_ref":1305,"_type":1164},"image-9e57868904f53753fd15d59194f2c5c7b33434d8-1763x1427-png",{"alt":9,"extension":87,"height":1307,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1303,"url":1308,"width":1253},1427,"https://cdn.sanity.io/images/ah5kp358/production/9e57868904f53753fd15d59194f2c5c7b33434d8-1763x1427.png",{"_key":1310,"_type":23,"children":1311,"markDefs":1316,"style":873},"c63e7e4684bf",[1312],{"_key":1313,"_type":27,"marks":1314,"text":1315},"d29de695f75e",[],"\nSome last words (for now!)",[],{"_key":1318,"_type":23,"children":1319,"markDefs":1333,"style":32},"d3303b8c9ffb",[1320,1324,1329],{"_key":1321,"_type":27,"marks":1322,"text":1323},"40b22cf5be9f",[],"Our main goal with this blog post series was to contribute with an evaluation protocol for AI Pentesting Agents that captures the properties that matter in real-world use. Although we also provide structured ground-truth for publicly available targets, the goal is not to define a static benchmark, nor do we claim that the targets used here form a complete evaluation suite. Our recommendation is simple: adopt the methodology and apply it to a realistic and varied set of targets that matches the intended application of the Pentesting Agent being evaluated. You can also help the community by contributing with your own targets and ground-truth to the ",{"_key":1325,"_type":27,"marks":1326,"text":1328},"35445181e746",[1327,29],"57b23bd256b3","EthiBench public repository",{"_key":1330,"_type":27,"marks":1331,"text":1332},"f0669f1a93fb",[],".",[1334],{"_key":1327,"_type":796,"href":800,"to":1335},{"_key":1327,"_type":11,"href":800,"type":11},{"_key":1337,"_type":23,"children":1338,"markDefs":1343,"style":32},"d4b0841b1ff7",[1339],{"_key":1340,"_type":27,"marks":1341,"text":1342},"84fd156443fc",[],"It is also important to note that other aspects of AI pentesting still need to be assessed beyond what we covered in this series. Real-world deployment demands evaluation of guardrails against destructive behavior, scope adherence, proof-of-concept quality, and related concerns. In fact, one could argue that safety assessment may be even more important than performance evaluation.",[],{"_key":1345,"_type":23,"children":1346,"markDefs":1351,"style":32},"626860790a1d",[1347],{"_key":1348,"_type":27,"marks":1349,"text":1350},"09b6b4226164",[],"More broadly, evaluation methodology is part of the deployment problem itself. If benchmarks reduce success to capture-the-flag tasks or single-goal exploitation, they may push practitioners toward systems that look strong in controlled settings but fail quietly on realistic targets with multiple vulnerabilities and more complex attack surfaces. Just as importantly, capable systems may be underrated because they perform poorly on tasks that do not reflect their real offensive utility. Making evaluation more realistic, finding-centered, and operationally informative helps ensure that the systems being trusted, improved, and deployed are the ones that actually matter in practice.",[],{"_key":1353,"_type":23,"children":1354,"markDefs":1359,"style":32},"986c28cd6f7e",[1355],{"_key":1356,"_type":27,"marks":1357,"text":1358},"b625b074fada",[],"This concludes our two-part series, but now that EthiBench also belongs to the community, we won't be silent for long. Stay 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