Every number on this page is re-checked against its public source once a week by sushant-me/reputation — a repository that lists each claim I make, names where it came from, and turns red the moment one stops being true. That badge is the result of that check, not decoration.

Agent safety you can measure. An autonomous agent that executes actions sits outside role-based access control, which authenticates an identity and says nothing about whether an action should happen. My current paper asks whether a small, locally served model can be that missing verification layer — 600 policy scenarios per condition on a 3.8B model inside a 4.2 GiB VRAM budget, no cloud inference. The headline result is the one that contradicted my own hypothesis: constraining output to JSON without a reasoning field made the evaluator less safe, not more (46.2% unsafe accepts against 17.2% unconstrained), and the paper reports the residual failures rather than rounding them away.

Tooling for the bug class I keep finding. agentbound statically detects tool-boundary bugs in agent frameworks; mcp-nameguard checks an MCP server's tool names against the ones frameworks reserve for themselves; trajectorycheck grades whole agent trajectories — tool calls, arguments, side effects and injected instructions — with a deliberately broken agent as a control.

The systems those agents stand on. Fuzzing and harness engineering, memory-safety triage under ASan/UBSan, and the ordinary C++ that decides whether an agent's world model can be crashed by 28 bytes.

Building for places where infrastructure is unreliable. Two years as an AI engineer (Atmos SoftTech) and an internship as technical lead: offline-first inference, a Wi-Fi CSI life-detection prototype that works through rubble without cameras, and an offline payment wallet.

• beyond-attention — a selective state-space model (Mamba/S6) implemented from scratch in NumPy, published with the results that don't flatter it: a scalar register still ties the agent on the arithmetic tasks, and the SSM is the weaker model past its training length.
• Edge-Native Semantic Firewall — paper, 600-scenario corpus, raw model outputs, camera-ready, and reproducibility checks that can fail.
• policygate — a fail-closed policy gate for agent tool calls, written because of a measurement: my paper found a locally served 3.8B model approving 23.5% of the proposals the policy would have blocked. In the library a model can deny or escalate and cannot authorise an uncovered action, a failing evaluator falls through to a refusal, and every decision lands in a hash-chained audit log. Ships adapters for MCP servers, LangChain-style tools and provider function calls, so gating a call needs no new dependency. Disabling either invariant fails three tests; disabling both fails six.
• mcpaudit — audits an MCP server's tool declarations before you connect: reserved-name collisions, instruction-shaped text, invisible Unicode with the tag-block payload decoded, look-alike names, destructive tools declaring readOnlyHint. Pins the declarations in a lock file, so a description that changes after you approve it is reported as the tool-poisoning shape it is.
• tool-boundary-corpus — a labelled corpus of 23 agent tool-boundary cases and a detector-agnostic harness, because a claim about a scanner is worth little without precision and recall. It scores three kinds: tool declarations, framework source, and declaration drift — a rug pull, which needs two states of the same server to be visible at all, so those cases carry the declarations as approved and as they are now. Measured: my mcpaudit at P=1.000 R=1.000 on the tool-list cases and on the drift cases, and agentbound at P=1.000 R=1.000 on the code cases. That last number started at 0.750: the corpus found a false positive in agentbound (a pattern inside a string bound to a name, which its comment-stripping did not cover), I fixed it in v0.1.10, and the same harness re-measured the improvement. Adding the guard that pins which build a score came from turned up a third defect — v0.1.11 corrects a release that reported the wrong version. The drift adapter was wrong before the detector was: it read the detector's findings key and dropped its drift key, scoring recall 0.000 on cases that passed — which is why the adapter is now tested against a fake detector emitting the real tool's JSON shape. The corpus is self-authored, and says so in every report.
• reputation — the claims-verification repository described above.

• A number nothing recomputes — ten wrong facts in things I had already published (a CV, a profile, a hire page, a paper's README, a released product), each true when written, and none of them visible to the claim verifier I already had. The fix is ~370 lines: forbidden strings carrying their reasons, required corrections so a fix-by-deletion fails, counts derived from the source file, and coverage asserted so a run that read nothing cannot pass. Includes the exemption I refused to add, and the three times the post failed its own check.
• A benchmark found a bug in my own detector — I built a labelled corpus to stop making unmeasured claims and it found a false positive in my own scanner: precision 0.750 → 1.000, with the failing case named in CI rather than deleted. The benchmark was wrong twice before the detector was, the harness assumed its author's output format, and the guard added for the fix surfaced a third defect — a release that reported the wrong version. Three defects, one shape: a number nothing checked.
• Structured output made my safety evaluator less safe — the counter-intuitive result from my paper: JSON-constrained output without a reasoning field approved 46.2% of proposals the policy would have blocked, against 17.2% for free-form, and it was 5.5× faster — the trade a team picks under a latency budget. Includes the parts that hurt: the best arm still approved 6 of 208 irreversible hard denials.
• A sample agent security review — a deliberately vulnerable agent I wrote, and the deliverable I would return for it: four findings (tool shadowing, a fail-open confirmation gate, indirect prompt injection, path traversal) with reproductions, fixes, and a section stating what was not tested. python3 demo.py reproduces all four.
• A sample MCP server audit — a deliberately risky vendor MCP server and the written audit I would return: a critical name collision that displaces a framework primitive, an instruction hidden in the Unicode tag block, a description telling the model to add account credentials to its summary, a destructive tool declaring readOnlyHint, and a look-alike name pair. Ten findings, two clean tools, the generated policy the gate enforces, a lock file that catches the tool-poisoning shape, and a demo.py that exits non-zero when a finding stops reproducing.
• The crash that wasn't — a memory-safety test that passed locally and segfaulted in CI, the AddressSanitizer trace that showed my patch fixed the wrong bug, and the pull request I closed on myself afterwards. "A passing memory-safety test proves almost nothing unless it runs under a sanitizer."

• agentbound — v0.1.11 · tool-boundary bugs in agent frameworks (Python, TypeScript, YAML)
• mcp-nameguard — v0.4.7 · MCP tool names against the ones frameworks reserve
• trajectorycheck — v0.1.2 · grades whole agent trajectories, with a deliberately broken agent as a control