{"slug": "proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability", "title": "ProofSec: Benchmarking Epistemic Robustness and Evidence-Grounded Vulnerability Reasoning in Frontier LLM", "summary": "A developer has released ProofSec, an evidence-centric security reasoning benchmark that tests whether large language models can distinguish security indicators from substantiating evidence. ProofSec v0.2 contains 110 security reasoning cases requiring models to classify scenarios as Vulnerable, Not Vulnerable, or Insufficient Evidence, with controlled evaluation families covering one-fact flips, evidence-state changes, and contradictions. The benchmark's central principle is that a security indicator is not equivalent to security evidence, and it evaluates whether models can recognize when a security conclusion is not epistemically justified.", "body_md": "**What happens when an LLM recognizes every lexical and semantic signature associated with a vulnerability - IDOR, BOLA, authorization bypass, predictable identifiers - but the available evidence does not actually establish that the vulnerability exists?**\n\nThat question is the foundation of **ProofSec**, an evidence-centric security reasoning benchmark designed to evaluate whether LLMs can distinguish **security indicators from substantiating evidence**, reason under incomplete and contradictory observations, resist terminology and authority bias, incorporate falsifying evidence, and explicitly recognize when a security conclusion is not epistemically justified.\n\nLarge language models have become exceptionally capable at semantic retrieval.\n\nGive a model:\n\n```\nGET /api/users/2841/invoices/9281\n```\n\nand introduce:\n\n```\npredictable numeric identifiers\n```\n\nand the model can immediately activate a large cybersecurity concept space:\n\n```\nIDOR\nBOLA\nBroken Access Control\nAuthorization Bypass\n```\n\nBut there is a fundamental distinction between **recognizing the semantic signature of a vulnerability** and **demonstrating that the underlying security property has actually been violated**.\n\nThat distinction is where many security reasoning systems become unreliable.\n\nConsider:\n\n```\nAuthenticated user: 2841\n\nGET /api/invoices/9281\n\nHTTP/1.1 200 OK\n\n{\n    \"invoice_id\": 9281,\n    \"amount\": 45000,\n    \"status\": \"paid\"\n}\n```\n\nIs this an IDOR?\n\nThe evidence is insufficient to establish that conclusion.\n\nThe observation does not independently establish:\n\n`9281`\nA predictable identifier is an **indicator**.\n\nAn HTTP `200` response is an **observation**.\n\nNeither fact, in isolation, constitutes proof of unauthorized cross-principal access.\n\nThe central principle behind ProofSec therefore became:\n\n**A security indicator is not equivalent to security evidence.**\n\nThis sounds straightforward.\n\nIn practice, it is a surprisingly difficult property for language models to maintain under adversarial framing, incomplete evidence, contradictory observations, and highly salient security terminology.\n\nProofSec evaluates **evidence-sensitive vulnerability classification**.\n\nEvery scenario requires the model to classify the security state into exactly one canonical category:\n\n```\nVulnerable\nNot Vulnerable\nInsufficient Evidence\n```\n\nThe third state is deliberately first-class.\n\nTraditional binary vulnerability classification implicitly encourages a forced decision:\n\n```\nYES\nor\nNO\n```\n\nReal security investigations rarely provide that luxury.\n\nEvidence can be incomplete.\n\nObservations can be ambiguous.\n\nTelemetry can be contradictory.\n\nAuthorization boundaries can be unknown.\n\nA response can be suspicious without being conclusive.\n\nProofSec therefore evaluates whether an LLM can recognize an **epistemic boundary**:\n\n**The available evidence is insufficient to establish the claim.**\n\nThis changes the benchmark from a conventional vulnerability-recognition exercise into an evaluation of:\n\nThe benchmark is therefore not primarily asking:\n\n\"Does the model know what IDOR means?\"\n\nIt is asking:\n\n**\"Can the model determine whether the evidence presented actually establishes the security property in question?\"**\n\nProofSec v0.2 contains **110 security reasoning cases**.\n\nThe corpus is deliberately constructed as an experimental evaluation set rather than an undifferentiated collection of cybersecurity questions.\n\nThe benchmark incorporates controlled evaluation families covering:\n\nThe architecture is designed around a core principle:\n\n**If the evidentiary state changes, the model should be sensitive to that change.**\n\nConceptually:\n\n```\n                         ProofSec\n                            │\n             ┌──────────────┼──────────────┐\n             │              │              │\n        One-Fact Flip   Evidence State   Contradiction\n             │              │              │\n             └──────────────┼──────────────┘\n                            │\n                    Security Scenario\n                            │\n                            ▼\n                      LLM Inference\n                            │\n                            ▼\n                  Structured Assessment\n                            │\n             ┌──────────────┼──────────────┐\n             │              │              │\n       Classification   Evidence State   Explanation\n             │\n             ▼\n       Deterministic Assertion\n             │\n             ▼\n        Numerical Score\n```\n\nThe implementation deliberately isolates:\n\n```\nDataset\n    ↓\nPrompt Construction\n    ↓\nModel Inference\n    ↓\nStructured Parsing\n    ↓\nAssertion\n    ↓\nScoring\n```\n\nThis separation is not cosmetic.\n\nIt establishes distinct **failure domains**.\n\nA dataset defect is not a model failure.\n\nA packaging failure is not a reasoning failure.\n\nA schema violation is not necessarily a classification failure.\n\nA scoring-interface defect is not a model-performance measurement.\n\nThat distinction became one of the most important engineering principles in the project.\n\nOne of the core mechanisms in ProofSec is controlled **one-fact perturbation**.\n\nInstead of constructing completely unrelated questions, ProofSec creates scenarios where a security-relevant fact changes while much of the surrounding semantic structure remains invariant.\n\nFor example:\n\n```\nAuthenticated user = 2841\nInvoice owner = 2841\n\nAuthorization rule:\ninvoice.owner_id == authenticated_user.id\nAuthenticated user = 2841\nInvoice owner = 9127\n\nAuthorization rule:\ninvoice.owner_id == authenticated_user.id\n```\n\nThe semantic surface can remain substantially similar.\n\nThe critical security relationship changes.\n\nThat gives us a controlled counterfactual:\n\n```\nΔ Input\n   ↓\nΔ Security-Relevant Fact\n   ↓\nExpected Δ Security State\n```\n\nThis is fundamentally more informative than asking:\n\n\"Is this an IDOR?\"\n\nThe actual evaluation question becomes:\n\n**Did the model detect the fact that causally changes the security state?**\n\nThis introduces a form of **counterfactual sensitivity testing**.\n\nIf one authorization-relevant fact changes and the model preserves the same classification, the benchmark exposes a specific failure mode.\n\nThe model may understand the terminology.\n\nIt may understand the vulnerability class.\n\nBut it may not be correctly conditioning its conclusion on the evidence that actually determines the security property.\n\nProofSec explicitly models the evidentiary state of a scenario.\n\nThe benchmark uses states including:\n\n```\nWEAK\nPARTIAL\nDECISIVE\nCONTRADICTORY\nNEGATIVE\nUNKNOWN\n```\n\nThis creates an evidence hierarchy rather than treating every security-relevant observation as equally probative.\n\nA simplified progression is:\n\n```\nPredictable identifier\n        │\n        ▼\nWeak security indicator\n        │\n        ▼\nOwnership relationship established\n        │\n        ▼\nCross-principal access demonstrated\n        │\n        ▼\nAuthorization invariant violated\n        │\n        ▼\nDECISIVE evidence\n```\n\nConsider the difference between:\n\n```\nPredictable ID\n```\n\nand:\n\n```\nUnauthorized cross-user object access\n```\n\nThese observations possess radically different evidentiary weight.\n\nThe first may justify investigation.\n\nThe second can establish a concrete violation of an authorization property.\n\nProofSec therefore attempts to prevent a model from collapsing the following distinction:\n\n```\nSuspicion\n    ≠\nEvidence\n    ≠\nProof\n```\n\nThat distinction is foundational to trustworthy security analysis.\n\nReal security investigations are not monotonic.\n\nEvidence can conflict.\n\nTelemetry can be stale.\n\nCaches can contain misleading artifacts.\n\nFixtures can resemble production responses.\n\nA preliminary observation can subsequently be invalidated by a higher-authority observation.\n\nProofSec therefore incorporates **contradiction-resolution scenarios**.\n\nA simplified reasoning sequence might look like:\n\n```\nInitial observation\n        │\n        ▼\nHTTP 200 response\n        │\n        ▼\nPotential authorization anomaly\n        │\n        ├───────────────┐\n        │               │\n        ▼               ▼\nSynthetic fixture   Actual endpoint\n        │               │\n        ▼               ▼\nCached response      HTTP 403\n        │               │\n        └───────┬───────┘\n                ▼\n       Re-evaluate hypothesis\n```\n\nThe important capability is not simply detecting the first suspicious observation.\n\nThe model must determine whether later evidence changes the validity of the original hypothesis.\n\nThis introduces a critical distinction between:\n\n```\nEvidence accumulation\nEvidence revision\n```\n\nA model that merely accumulates confirming signals can behave very differently from one capable of **hypothesis revision under contradictory evidence**.\n\nProofSec deliberately tests that boundary.\n\nMany vulnerability benchmarks are heavily oriented toward positive findings.\n\nProofSec deliberately gives **negative evidence** first-class status.\n\n```\nUser A requests User B's object\n                │\n                ▼\nAuthorization rule verified\n                │\n                ▼\nCross-user request returns 403\n                │\n                ▼\nUnauthorized access not demonstrated\n```\n\nA security reasoning system must be able to update its hypothesis in both directions.\n\nIt must identify:\n\n```\nEvidence supporting vulnerability\nEvidence weakening or falsifying vulnerability hypothesis\n```\n\nThis matters because security investigation is fundamentally adversarial.\n\nThe objective is not to collect evidence that confirms the initial hypothesis.\n\nThe objective is to determine whether the hypothesis survives attempts to falsify it.\n\nThat makes negative evidence an important component of **hypothesis discrimination**.\n\nAnother failure mode targeted by ProofSec is **authority and terminology bias**.\n\nCybersecurity vocabulary carries enormous semantic weight.\n\nTerms such as:\n\n```\ncritical\nconfirmed\nexploit\nCVE\nresearcher\nprivilege escalation\nauthorization bypass\nsecurity issue\n```\n\ncan exert disproportionate influence over LLM outputs.\n\nProofSec therefore evaluates whether the model remains anchored to technical evidence when the surrounding terminology changes.\n\nThe underlying principle is:\n\n**Terminology describes a claim. Evidence substantiates it.**\n\nConsider two descriptions:\n\n```\nPossible authorization issue\nConfirmed critical authorization vulnerability\n```\n\nIf the underlying technical evidence is unchanged, the semantic framing should not arbitrarily alter the actual security state.\n\nThis probes **linguistic framing sensitivity** and **authority-induced classification drift**.\n\nThe benchmark is therefore not only testing cybersecurity knowledge.\n\nIt is testing whether that knowledge remains subordinate to the evidentiary record.\n\nI intentionally avoided unconstrained free-form text as the primary evaluation interface.\n\nEach model response is constrained into a structured schema containing:\n\n```\nclassification\nevidence_state\nsupporting_evidence\nmissing_evidence\nsafe_verification\nimpact\n```\n\nThis creates a deterministic machine-readable boundary between inference and evaluation.\n\nThe primary scoring target is:\n\n```\nclassification\n```\n\nThe remaining fields provide diagnostic observability.\n\n```\nclassification:\nVulnerable\n```\n\ncould be accompanied by:\n\n```\nsupporting_evidence:\n\"The identifier is sequential.\"\n```\n\nThat exposes a potentially significant evidentiary defect:\n\n```\nPredictable Identifier\n        ≠\nProven Unauthorized Access\n```\n\nThe final classification alone cannot reveal whether the model arrived at the conclusion through valid or invalid reasoning.\n\nStructured assessment therefore provides a richer **diagnostic surface**.\n\nI deliberately avoided making another LLM the primary arbiter of the classification.\n\nProofSec uses canonical ground-truth labels and deterministic assertions.\n\nThe core evaluation chain is:\n\n```\nScenario\n   ↓\nModel\n   ↓\nStructured Classification\n   ↓\nCanonical Ground Truth\n   ↓\nExact Assertion\n   ↓\nScore\n```\n\nrather than:\n\n```\nScenario\n   ↓\nModel A\n   ↓\nModel B judges Model A\n   ↓\nScore\n```\n\nFor the core classification metric, deterministic evaluation establishes a cleaner experimental boundary.\n\nIt also reduces the possibility of **evaluation contamination**, where the judgment model introduces another layer of model-dependent interpretation into the primary metric.\n\nA benchmark is only scientifically useful if its evaluation corpus is reproducible.\n\nProofSec v0.2 uses a frozen dataset representation with a SHA-256 integrity digest:\n\n```\n422501a4db424c30c8ef24b61183351ec8a4bd2096e2671cf0e6bdf91e133a80\n```\n\nThe project maintains a manifest and an integrity-verification workflow.\n\nThe intended reproducibility invariant is:\n\n```\nSame benchmark version\n        +\nSame task corpus\n        +\nSame evaluation protocol\n        =\nComparable experiment\n```\n\nWithout corpus integrity, two apparently identical experiments can silently evaluate different datasets.\n\nThat means dataset provenance, versioning, and integrity verification are part of the experimental methodology itself.\n\nOne of the most valuable discoveries during the project was that **benchmark engineering is itself part of evaluation science**.\n\nMy initial task implementation used:\n\n``` php\n-> None\n```\n\nwith assertions.\n\nThat produced pass/fail-style behavior.\n\nI initially expected the benchmark to expose a numerical accuracy metric.\n\nThat assumption was incorrect.\n\nI subsequently changed the task interface to:\n\n``` php\n-> float\n```\n\nand explicitly returned:\n\n```\naccuracy = correct / total\nreturn float(accuracy)\n```\n\nThis exposed a broader principle:\n\n**The semantics of the evaluation harness are part of the experimental design.**\n\nA benchmark can execute successfully while still reporting an invalid measurement if the scoring contract is incorrectly defined.\n\nIn other words:\n\n```\nCorrect Inference\n        ≠\nCorrect Evaluation\n```\n\nA trustworthy benchmark requires both.\n\nOne early published iteration failed with:\n\n```\nNameError: name 'ALL_TASKS_JSON' is not defined\n```\n\nThis was not a model reasoning failure.\n\nThe model never reached the reasoning stage.\n\nThe task failed during execution before the first model inference step.\n\nThat forced a more rigorous separation of:\n\n```\nDataset Integrity\n        │\n        ▼\nTask Packaging\n        │\n        ▼\nRuntime Execution\n        │\n        ▼\nModel Inference\n        │\n        ▼\nStructured Parsing\n        │\n        ▼\nAssertion\n        │\n        ▼\nScoring\n```\n\nThis distinction is fundamental.\n\nA benchmark must not silently transform:\n\n```\nRuntime Error\n```\n\ninto:\n\n```\nModel Error\n```\n\nThose are different failure classes with different remediation paths.\n\nIt also exposed an important deployment boundary.\n\nMy local development environment contained the canonical task source under:\n\n```\nC:\\Users\\User\\Documents\\ProofSec\\kaggle\\tasks\n```\n\nbut a Kaggle-hosted execution environment cannot directly access that Windows filesystem.\n\nThe benchmark therefore has to package the necessary evaluation corpus and task implementation into the executable artifact available to the remote runtime.\n\nThat became an important lesson in **benchmark portability and execution-environment isolation**.\n\nThe benchmark went through multiple iterations while I validated the evaluation infrastructure.\n\nOne development version displayed assertion counts including:\n\n```\nClaude Sonnet 4.6    0 Pass\nGPT-5.5             75 Pass\nGemini 3.5 Flash    95 Pass\nGemini 3.7 Flash    87 Pass\n```\n\nA subsequent controlled version successfully executed its smoke-test assertions across the evaluated models.\n\nThese development-stage measurements should not be interpreted as a universal model ranking.\n\nThey represent different stages of benchmark engineering.\n\nDuring this process I was simultaneously validating:\n\nThis separation is essential for responsible interpretation.\n\nA benchmark-development artifact is not automatically equivalent to a final scientific measurement.\n\nBefore treating Kaggle execution as authoritative, I also used local evaluation to inspect the complete 110-case corpus.\n\nRepresentative local validation produced:\n\n| Model | Correct | Total | Accuracy | \n|---|---|---|---|\n| Claude Sonnet 4.6 | 80 | 110 | 72.73% | \n| Gemini 3.7 Flash | 86 | 110 | 78.18% | \n| Gemini 3.5 Flash | 84 | 110 | 76.36% | \n| GPT-5.5 | 67 | 110 | 60.91% | \n\nThese figures are **local validation measurements under the specific execution conditions used during development**.\n\nThey should not be interpreted as immutable measurements of model capability.\n\nLLM inference can vary because of:\n\nThis variability is itself relevant to reproducibility.\n\nThe objective was therefore not to reduce the benchmark to:\n\n\"Model X is better.\"\n\nThe objective was to characterize **failure modes, evidentiary behavior, and reasoning sensitivity under a controlled corpus**.\n\nOne of the recurring failure patterns motivating ProofSec can be represented as:\n\n```\nSecurity Vocabulary Recognition\n              ↓\n       Semantic Activation\n              ↓\n     Premature Classification\n              ↓\n Evidence Is Never Actually Tested\n```\n\nThe desired reasoning pathway is:\n\n```\nScenario\n   ↓\nExtract Security-Relevant Claims\n   ↓\nIdentify Actors and Authorization Boundaries\n   ↓\nIdentify Directly Observed Facts\n   ↓\nSeparate Indicators from Decisive Evidence\n   ↓\nEvaluate Contradictory Evidence\n   ↓\nEvaluate Negative Evidence\n   ↓\nDetermine Evidentiary Sufficiency\n   ↓\nClassify\n```\n\nThe distinction is fundamental.\n\nA model can possess extensive cybersecurity knowledge while still being unreliable at **evidence-grounded adjudication**.\n\nThis is the capability ProofSec attempts to isolate.\n\nThis is one of the most consequential design decisions in ProofSec.\n\nA conventional benchmark might ask:\n\n```\nIs the system vulnerable?\n\nYES / NO\n```\n\nProofSec instead represents:\n\n```\nVulnerable\nNot Vulnerable\nInsufficient Evidence\n```\n\nThis explicitly models **epistemic uncertainty**.\n\nThe model must distinguish between:\n\n```\nObserved\nEstablished\n```\n\nThat distinction matters operationally.\n\n```\nUnsupported Finding\n        ↓\nAnalyst Investigation\n        ↓\nEngineering Interruption\n        ↓\nPotential Remediation\n        ↓\nOperational Cost\n```\n\nA system that generates large quantities of unsupported security findings can impose substantial downstream cost even when its vulnerability-recognition capability appears impressive.\n\nAbstention is therefore not merely a failure to classify.\n\nUnder uncertainty, abstention can be a legitimate and necessary security behavior.\n\nAt a deeper level, ProofSec is not simply a cybersecurity benchmark.\n\nIt evaluates whether an LLM can maintain an explicit boundary between:\n\n```\nWhat do I know?\n        ↓\nWhat does the evidence establish?\n        ↓\nWhat remains unknown?\n        ↓\nWhat conclusion is justified?\n```\n\nThis is fundamentally an **epistemic reasoning problem**.\n\nCybersecurity provides a particularly concrete environment in which to measure it because security conclusions often have direct operational consequences.\n\nThe same methodology could potentially extend to:\n\nThe security domain is simply where I chose to operationalize the problem.\n\nProofSec is implemented using:\n\n`kaggle_benchmarks`\nThe architecture maintains explicit boundaries between:\n\n```\nDATA\nPROMPT CONSTRUCTION\nMODEL INFERENCE\nSTRUCTURED PARSING\nEVALUATION\nSCORING\n```\n\nThis makes the system substantially easier to:\n\nThe benchmark is therefore treated as an engineered evaluation artifact rather than merely a collection of prompts.\n\nThe development workflow became:\n\n```\nSecurity Hypothesis\n        ↓\nConstruct Controlled Scenario\n        ↓\nDefine Canonical Ground Truth\n        ↓\nGenerate Adversarial / Paired Variant\n        ↓\nValidate Dataset\n        ↓\nFreeze Corpus\n        ↓\nGenerate Integrity Hash\n        ↓\nImplement Structured Task\n        ↓\nRun Local Validation\n        ↓\nValidate Kaggle Packaging\n        ↓\nExecute Against Multiple Models\n        ↓\nInspect Assertions\n        ↓\nAnalyze Failure Modes\n        ↓\nRevise Benchmark Infrastructure\n```\n\nThis treats the benchmark as a versioned research artifact.\n\nThe project structure is maintained as an engineering repository containing the benchmark implementation, evaluation infrastructure, integrity controls, and supporting documentation.\n\nThe objective is to make the benchmark auditable rather than opaque.\n\nThe presence of:\n\n```\nID\nendpoint\nHTTP 200\nuser\ninvoice\n```\n\ndoes not automatically establish IDOR.\n\nThe authorization relationship is the security property that matters.\n\nIf a single security-relevant fact changes, the model should respond to that change.\n\nControlled perturbation therefore provides a much stronger probe of reasoning sensitivity than simply increasing the number of unrelated benchmark questions.\n\nA security reasoning system must be capable of recognizing evidence that weakens or falsifies its initial hypothesis.\n\nA model that performs well under clean, monotonic evidence may behave very differently when observations conflict.\n\nContradiction handling is therefore a meaningful robustness dimension.\n\n\"Insufficient Evidence\" can be treated as an explicit epistemic state rather than an evasive response.\n\nA classification tells us:\n\n```\nWhat did the model decide?\n```\n\nA structured assessment can additionally expose:\n\n```\nWhat evidence did it identify?\nWhat evidence did it consider missing?\nWhat verification did it propose?\n```\n\nThat creates a substantially richer diagnostic surface.\n\nDataset defects, packaging failures, schema violations, runtime exceptions, parsing failures, and model reasoning failures require separate failure categories.\n\nA benchmark without corpus integrity, version control, and execution controls can silently drift.\n\nA score without provenance is considerably less useful than a score attached to a reproducible experimental configuration.\n\nProofSec v0.2 focuses on controlled evidence reasoning.\n\nThe next iteration can extend the methodology considerably.\n\nInstead of presenting all evidence simultaneously:\n\n```\nT0 → Initial Telemetry\nT1 → Additional Observation\nT2 → Contradictory Artifact\nT3 → Authorization Test\n```\n\nI want to evaluate whether the model updates its hypothesis correctly as evidence arrives.\n\nThis transforms static classification into a **sequential evidence-update problem**.\n\nInstead of evaluating only:\n\n```\nclassification\n```\n\nevaluate:\n\n```\nclassification\n+\nconfidence\n```\n\nThen measure whether confidence is statistically aligned with empirical correctness.\n\nA model that is wrong with extremely high confidence represents a fundamentally different operational risk profile from a model that identifies uncertainty.\n\nRequire the model to answer:\n\n```\nWhich exact fact changed your classification?\n```\n\nThis enables a deeper evaluation:\n\n```\nCorrect Conclusion\n        +\nCorrect Evidentiary Basis\n```\n\nversus:\n\n```\nCorrect Conclusion\n        +\nIncorrect Evidentiary Basis\n```\n\nThis distinction matters because a model can occasionally arrive at the correct answer for the wrong reasons.\n\nThe framework can be extended into:\n\n```\nSSRF\nAuthentication Bypass\nPrivilege Escalation\nCSRF\nPath Traversal\nRace Conditions\nBusiness Logic Flaws\nCloud IAM\nAPI Authorization\nMulti-Tenant Isolation\n```\n\nThe broader research question becomes:\n\n**Is evidence-grounded security reasoning a generalizable capability, or is it primarily a collection of domain-specific semantic associations?**\n\nWhen I started building ProofSec, the obvious question was:\n\n**Which LLM is better at cybersecurity?**\n\nAfter engineering the benchmark, that no longer seemed like the most interesting question.\n\nThe more fundamental question is:\n\n**Can we construct evaluations that determine whether an AI security conclusion is actually justified by the evidence available to it?**\n\nA leaderboard such as:\n\n```\nModel A - 82%\nModel B - 79%\nModel C - 76%\n```\n\nis useful.\n\nBut it is incomplete.\n\nFor an AI security system, I want to know:\n\n```\nWhat evidence did the model use?\n\nWhat evidence did it ignore?\n\nDid it infer facts that were never provided?\n\nDid it confuse an indicator with proof?\n\nDid it recognize negative evidence?\n\nDid it resolve contradictions?\n\nDid terminology alter its classification?\n\nDid it respond to a one-fact perturbation?\n\nDid it revise its hypothesis?\n\nDid it recognize when the evidence was insufficient?\n```\n\nThose questions are much closer to the requirements of a real security-analysis system.\n\n**ProofSec v0.2** contains **110 security reasoning cases** designed around controlled evidence manipulation, structured classification, deterministic ground truth, integrity verification, and reproducible multi-model evaluation.\n\nThe benchmark is built around one principle:\n\n**Security conclusions should be proportional to the evidence supporting them.**\n\nProofSec operationalizes that principle through:\n\nThe benchmark implementation and supporting engineering work are available on GitHub:\n\nThe repository is intended to make the benchmark more than a Kaggle submission.\n\nIt provides an engineering artifact around the evaluation methodology, allowing the benchmark to be inspected, versioned, reproduced, and extended independently of the Kaggle interface.\n\nThe broader objective is to preserve the chain:\n\n```\nResearch Hypothesis\n        ↓\nDataset Construction\n        ↓\nGround Truth\n        ↓\nEvaluation Harness\n        ↓\nIntegrity Verification\n        ↓\nModel Execution\n        ↓\nResults\n        ↓\nFailure Analysis\n```\n\nThat provenance matters.\n\nA benchmark should not only report a number.\n\nIt should make it possible to understand **where that number came from**.\n\nThe most important result from building ProofSec was not a single model score.\n\nIt was discovering how easily an evaluation can appear to measure security reasoning while actually measuring something else.\n\nA benchmark can accidentally measure:\n\n```\nKeyword Recognition\n```\n\ninstead of:\n\n```\nEvidence Reasoning\n```\n\nIt can measure:\n\n```\nRuntime Correctness\nModel Correctness\nConfidence\nEvidence-Justified Certainty\nSemantic Familiarity\nCausal Sensitivity\n```\n\nAnd it can report a precise-looking number while the underlying dataset, runtime, scoring semantics, or evaluation protocol is not actually controlled.\n\nProofSec is my attempt to make those boundaries explicit.\n\nThe objective is not simply to build an LLM that identifies the largest number of possible vulnerabilities.\n\nThe objective is to construct an evaluation framework capable of answering a substantially harder question:\n\n**When an AI claims that a security vulnerability exists, did the available evidence actually justify that claim?**\n\nThat is the standard I want AI-assisted security tooling to eventually be held to.\n\nNot:\n\n**Did the model recognize the vulnerability vocabulary?**\n\nBut:\n\n**Did the model establish the security claim from the evidence?**\n\nThat distinction is the entire premise of ProofSec.\n\n**ProofSec Benchmark**\n\n**ProofSec Source Repository**\n\n**Kaggle Benchmarking Challenge**\n\n[Kaggle Benchmarking Challenge on DEV](https://dev.to/challenges/kaggle-2026-09-23)", "url": "https://wpnews.pro/news/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability", "canonical_source": "https://dev.to/anuththara2007w/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability-reasoning-in-24bm", "published_at": "2026-09-29 11:03:28+00:00", "updated_at": "2026-09-29 11:16:54.143527+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research", "ai-tools"], "entities": ["ProofSec"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability", "markdown": "https://wpnews.pro/news/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability.md", "text": "https://wpnews.pro/news/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability.txt", "jsonld": "https://wpnews.pro/news/proofsec-benchmarking-epistemic-robustness-and-evidence-grounded-vulnerability.jsonld"}}