Fetching from the wire…
Public story · 2026-08-10 · high
Generating the proof takes about 43 times longer than verifying it, deciding where in an agent's loop it can run.
Why now: It's new work, tested only against one benchmark so far, with no report yet of production use.
NiyamAI wraps every AI agent tool call in a cryptographic proof, per a paper posted to arXiv as 2608.07167.
Teams giving an agent access to tools have to decide how much to trust an automated approval. NiyamAI's numbers give them one data point: on Agent-SafetyBench, it catches unsafe tool calls with an F1 score of 88.5% at a 1.1% false-positive rate.
Before a tool call runs, the system checks it against a SHA-256-locked set of rules called an Intent Contract. A separate, isolated model acts as judge. NiyamAI then generates a zk-SNARK proof, via EZKL, that the check happened, without revealing anything about the underlying model's weights.
The catch is speed. Generating each proof takes 2260.6 milliseconds on average. Verifying one takes 53.1 milliseconds, roughly 43 times faster. Two and a quarter seconds is a tolerable tax on a single high-stakes, irreversible tool call. It's a dealbreaker for an agent calling tools one after another in a loop.
Each link below shares sources, entities, or timing with this story.
arXiv 2608.09885 treats the harness as the thing that evolves with emerging risk rather than a static wrapper around a model you keep re-aligning. Four artifacts with non-overlapping responsibilities: System Prompt, Rule Bank, Safety Memory, Tool Policy. Failures get attribute...
A prevalence study of 10,008 public GitHub repos found 10.1% of tracked agent config paths (rules files, agent definitions, CLAUDE.md-style markdown) are SHA-256 exact duplicates across independent repos, with 75.5% of clone pairs near-identical. Agent configurations are propa...
The paper names it inertia bias: once an agent has produced a query, plan or intermediate conclusion, it judges the consequences of that action less objectively (arXiv 2608.23045). The IBIS benchmark isolates the effect by holding search observations fixed while varying whethe...
An edit cannot un-authorize a permission already granted or un-send a tool request already in flight, and the paper shows an unsafe edit can authorize the same action twice, discard a result the task still needs, or conflict with a call that started before the edit (arXiv 2608...
arXiv 2607.29167 describes the mechanism precisely: when an agent consolidates an external observation into long-term memory, the rewrite preserves the action trigger while erasing the low-trust source. The injected instruction resurfaces later looking like user history. Memor...
PCAS: Policy Compiler for Secure Agentic Systems — The first paper to provide measured enforcement results for agent policy compliance (48% to 93%). Uses dependency graphs and Datalog-derived policy language with a reference monitor intercepting all actions. Three case studies...
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