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The current evaluation covers 108 expert-annotated vulnerabilities across three targets: 60 in ",{"_key":930,"_type":27,"marks":931,"text":933},"s010b",[29,932],"l004","vuln-bank",{"_key":935,"_type":27,"marks":936,"text":937},"s010c",[],", 28 in ",{"_key":939,"_type":27,"marks":940,"text":942},"s010d",[29,941],"l005","paygoat",{"_key":944,"_type":27,"marks":945,"text":946},"s010e",[],", and 20 in ",{"_key":948,"_type":27,"marks":949,"text":951},"s010f",[29,950],"l006","blog",{"_key":953,"_type":27,"marks":954,"text":955},"s010g",[]," (aka xben-090). That last one is a nice illustration of the whole argument. It comes originally from a CTF-style challenge, and when you evaluate it as a pentesting environment instead of a flag race, it turns out to contain 18 validated vulnerabilities that flag-based scoring is blind to. To be clear: we're not saying these three targets alone are enough for a robust benchmark. They're the current expert-annotated starting point, and EthiBench is an adaptable protocol rather than a fixed target list — the ground truth is designed to grow, and you can point the framework at your own targets.",[957,960,963],{"_key":932,"_type":851,"href":958,"to":959},"https://github.com/Commando-X/vuln-bank",{"_key":932,"_type":11,"href":958,"type":11},{"_key":941,"_type":851,"href":961,"to":962},"https://github.com/stuxctf/PAYGoat",{"_key":941,"_type":11,"href":961,"type":11},{"_key":950,"_type":851,"href":964,"to":965},"https://github.com/xbow-engineering/validation-benchmarks/tree/main/benchmarks/XBEN-090-24",{"_key":950,"_type":11,"href":964,"type":11},{"_key":967,"_type":23,"children":968,"markDefs":973,"style":921},"b011",[969],{"_key":970,"_type":27,"marks":971,"text":972},"s011",[],"Semantic matching instead of string matching",[],{"_key":975,"_type":23,"children":976,"markDefs":981,"style":32},"b012",[977],{"_key":978,"_type":27,"marks":979,"text":980},"s012",[],"When an agent reports a vulnerability, deciding whether it corresponds to a known ground-truth entry is a judgment call, and the same real vulnerability can be described in many ways. EthiBench scores reports through a pipeline that combines LLM-as-judge semantic matching with bipartite resolution (solved with the Hungarian algorithm), so each reported vulnerability maps to at most one ground-truth entry and duplicates can't inflate recall. The judge itself was sanity-checked against human experts: on a set of 50 expert-triaged reports, the selected judge model misclassified roughly one on average.",[],{"_key":983,"_type":23,"children":984,"markDefs":989,"style":921},"b013",[985],{"_key":986,"_type":27,"marks":987,"text":988},"s013a",[],"Scoring all vulnerabilities",[],{"_key":991,"_type":23,"children":992,"markDefs":997,"style":32},"b014",[993],{"_key":994,"_type":27,"marks":995,"text":996},"s014",[],"Agents sometimes validate real vulnerabilities the annotators missed. A static answer key would score those as false positives and punish the agent for doing its job really well. EthiBench on the other hand, treats ground truth as a continuously maintained resource that expands when a novel, validated vulnerability survives review. So when an agent finds a vulnerability that annotators missed, it is rewarded.",[],{"_key":999,"_type":23,"children":1000,"markDefs":1005,"style":921},"b015",[1001],{"_key":1002,"_type":27,"marks":1003,"text":1004},"s015",[],"Dealing with non-determinism",[],{"_key":1007,"_type":23,"children":1008,"markDefs":1013,"style":32},"b016",[1009],{"_key":1010,"_type":27,"marks":1011,"text":1012},"s016",[],"We all know that agent runs are non-deterministic. The same agent on the same target produces different vulnerability sets on different runs, which means a single run isn't enough to tell you anything useful. EthiBench evaluates both per-run performance and cumulative performance across repeated runs, and pairs Welch's t-test with Cohen's d, because with high run-to-run variance and few replicates, p-values alone give misleading results. The paper has a concrete example: one agent beats another on F1 by 12.35%, and the p-value (0.0577) just misses the conventional significance threshold. Cohen's d for the same comparison is 2.168, a large effect by any standard. Rely on the p-value alone and you'd dismiss a real performance gap as noise.",[],{"_key":1015,"_type":23,"children":1016,"markDefs":1021,"style":921},"b017",[1017],{"_key":1018,"_type":27,"marks":1019,"text":1020},"s017",[],"The rest of the protocol",[],{"_key":1023,"_type":23,"children":1024,"markDefs":1029,"style":32},"b018",[1025],{"_key":1026,"_type":27,"marks":1027,"text":1028},"s018",[],"These four are the biggest shifts, but not the whole protocol. EthiBench also supports cumulative evaluation across repeated runs, temporal evaluation for tracking how performance evolves over time, and correlation-based subset selection for building smaller target sets that still predict full-benchmark results. The first two posts in the series cover each of these in more depth.",[],{"_key":1031,"_type":23,"children":1032,"markDefs":1037,"style":894},"b019",[1033],{"_key":1034,"_type":27,"marks":1035,"text":1036},"s019",[],"What to measure, exactly?",[],{"_key":1039,"_type":23,"children":1040,"markDefs":1045,"style":32},"b020",[1041],{"_key":1042,"_type":27,"marks":1043,"text":1044},"s020",[],"Having the more dynamic approach above gives a more realistic view of the agent's real-world hacking capabilities, but then how do we actually compare agents with each other?",[],{"_key":1047,"_type":23,"children":1048,"markDefs":1053,"style":32},"b021",[1049],{"_key":1050,"_type":27,"marks":1051,"text":1052},"s021",[],"In the previous two posts we put four systems through the protocol: the open-source pentesting engines Strix and PentAGI, the general-purpose agentic coding system Claude Code, and our own agent Hackian, each across multiple LLM backends, fully black-box. The agent gets a target URL and an instruction to pentest it, and nothing else: three runs per setup, with a fresh environment each time.",[],{"_key":1055,"_type":23,"children":1056,"markDefs":1061,"style":32},"b022",[1057],{"_key":1058,"_type":27,"marks":1059,"text":1060},"s022",[],"Across those 108 vulnerabilities, no single number describes any agent honestly, and performance varies a lot depending on the configuration, both in finding vulnerabilities and in validating them correctly. As we showed across the first two posts, averaged over three independent runs, our own agent Hackian led on both F1 and recall under the same protocol. That gap is not noise: against Claude Code on the same Sonnet 4.6 backend, Hackian's F1 advantage is 12.28% (p = 0.0206, Cohen's d = 3.048), and against PentAGI-Sonnet it is 16.72% (p = 0.0218, d = 3.297). Hackian isn't in the paper itself, since the peer-reviewed evaluation covers third-party systems only, which is exactly why the previous posts ran the comparison again with Hackian included. We know \"our agent wins on our benchmark\" is exactly the kind of claim this post tells you to distrust, which is why the protocol, the targets, and the ground truth are all public. Run it yourself and check.",[],{"_key":1063,"_type":23,"children":1064,"markDefs":1069,"style":32},"b023",[1065],{"_key":1066,"_type":27,"marks":1067,"text":1068},"s023",[],"The third-party systems each trade off differently. Strix achieves high precision, close to perfect in some configurations, but at the cost of a significantly lower share of valid findings reported: much of the known attack surface goes untouched in a single run. Claude Code (running Claude Sonnet 4.6) is the strongest third-party setup on F1 and recall, and it runs noisier. Part two's temporal analysis shows what that noise looks like in practice: tracked over the course of a run on paygoat, its false positives begin to accumulate rapidly in the latter half of the runtime, a drop in validation quality that end-of-run metrics can't capture. PentAGI-Sonnet sits between them on F1, ahead of Strix-Sonnet on recall while giving up 25.70% of precision to it.",[],{"_key":1071,"_type":905,"asset":1072,"image":1074},"e84b6f288d60",{"_ref":1073,"_type":908},"image-f24de6227e65c20eeab7454f51c93ab7157e4f53-2048x1499-jpg",{"alt":9,"extension":78,"height":1075,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1071,"url":1076,"width":912},1499,"https://cdn.sanity.io/images/ah5kp358/production/f24de6227e65c20eeab7454f51c93ab7157e4f53-2048x1499.jpg",{"_key":1078,"_type":23,"children":1079,"markDefs":1084,"style":32},"b024",[1080],{"_key":1081,"_type":27,"marks":1082,"text":1083},"s024",[],"Repeated runs redraw the picture again. Because agent runs are stochastic, different runs of the same setup surface different vulnerabilities, so aggregating findings across runs can lift recall, but it can also accumulate false positives and drag precision down. On the blog target, two engines on the same model respond in opposite ways to repeated testing: Strix-Sonnet shows a strong F1 increase when findings from three runs are aggregated, while PentAGI-Sonnet's cumulative score falls as accumulated false positives outweigh its recall gains. The detection overlap explains why: 75% of PentAGI-Sonnet's true positives were shared across all three runs, while Strix-Sonnet's runs overlapped far less, so each additional run kept contributing genuinely new findings. A mean and a standard deviation alone won't show you any of this.",[],{"_key":1086,"_type":905,"asset":1087,"image":1089},"53a913d21fca",{"_ref":1088,"_type":908},"image-8b6393901c658f7b4daa2e4cd7fa1fdc9c6fc943-2048x1480-jpg",{"alt":9,"extension":78,"height":1090,"hotspot":9,"secondaryCaption":9,"type":80,"uid":1086,"url":1091,"width":912},1480,"https://cdn.sanity.io/images/ah5kp358/production/8b6393901c658f7b4daa2e4cd7fa1fdc9c6fc943-2048x1480.jpg",{"_key":1093,"_type":23,"children":1094,"markDefs":1099,"style":32},"b025",[1095],{"_key":1096,"_type":27,"marks":1097,"text":1098},"s025",[],"Each of those profiles wins under some benchmark design and loses under another. A leaderboard that ranks on precision crowns Strix while most of the attack surface goes untested. One that ranks on raw discovery rewards noise. Discovery rate, false positive burden, and severity coverage trade off against each other, and any evaluation that collapses them into one score is hiding the trade-off from you, whichever agent it happens to favour. None of this is meant as a definitive ranking; it's a demonstration of what the protocol reveals that a single leaderboard number can't.",[],{"_key":1101,"_type":23,"children":1102,"markDefs":1107,"style":32},"b026",[1103],{"_key":1104,"_type":27,"marks":1105,"text":1106},"s026",[],"When you think about it, different agents excel in different areas, and each of them is more useful in specific scenarios. For anyone procuring or building these systems, the useful question changes. It's almost as useful to ask how a tool was evaluated as it is to ask how it scored.",[],{"_key":1109,"_type":23,"children":1110,"markDefs":1115,"style":894},"b027",[1111],{"_key":1112,"_type":27,"marks":1113,"text":1114},"s027",[],"Test your own agent",[],{"_key":1117,"_type":23,"children":1118,"markDefs":1123,"style":32},"b028",[1119],{"_key":1120,"_type":27,"marks":1121,"text":1122},"s028",[],"Benchmarking tools are only really useful if everyone can use them, so we made EthiBench open.",[],{"_key":1125,"_type":23,"children":1126,"markDefs":1149,"style":32},"b029",[1127,1131,1136,1140,1145],{"_key":1128,"_type":27,"marks":1129,"text":1130},"s029a",[],"Strictly speaking, it's an evaluation protocol rather than a fixed leaderboard: because it scores reported vulnerabilities instead of agent internals or source-code access, it works across white-box, gray-box, and black-box testing, and you can point it at your own targets. The ",{"_key":1132,"_type":27,"marks":1133,"text":1135},"s029b",[1134],"l007","repository",{"_key":1137,"_type":27,"marks":1138,"text":1139},"s029c",[]," has the harness, the target definitions, and the evaluation pipeline, and the ",{"_key":1141,"_type":27,"marks":1142,"text":1144},"s029d",[1143],"l008","paper",{"_key":1146,"_type":27,"marks":1147,"text":1148},"s029e",[]," has the full methodology and results.",[1150,1153],{"_key":1134,"_type":851,"href":1151,"to":1152},"https://github.com/ethiack/ethibench",{"_key":1134,"_type":11,"href":1151,"type":11},{"_key":1143,"_type":851,"href":852,"to":1154},{"_key":1143,"_type":11,"href":852,"type":11},{"_key":1156,"_type":23,"children":1157,"markDefs":1162,"style":32},"b030",[1158],{"_key":1159,"_type":27,"marks":1160,"text":1161},"s030",[],"If you build or deploy an AI pentesting agent, you can easily run it against this benchmark and see what the old leaderboards were missing. If you disagree with how we've drawn the methodology, even better: open an issue and argue with us! Our ultimate goal is to make the best AI pentesting agent benchmark methodology possible.",[],{"_key":1164,"_type":23,"children":1165,"markDefs":1170,"style":32},"b031",[1166],{"_key":1167,"_type":27,"marks":1168,"text":1169},"s031",[],"We feel that the field settled on benchmarks that were measuring the wrong things. EthiBench is our attempt to close that gap. 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