parlov-analysis

Signal detection and oracle classification for parlov. Pure synchronous computation — no I/O, no async, no network stack.

trait

pub enum SampleDecision {
    Complete(OracleResult),
    NeedMore,
}

pub trait Analyzer: Send + Sync {
    fn evaluate(&self, data: &DifferentialSet) -> SampleDecision;
    fn oracle_class(&self) -> OracleClass;
}

The caller drives an adaptive loop — collect exchange pairs, call evaluate(), stop when Complete. All oracle semantics (how many samples, stability criteria, classification) live in the analyzer. The DifferentialSet carries technique context (vector, normative strength) so the analyzer can extract typed signals and score confidence.

use it

use parlov_analysis::existence::ExistenceAnalyzer;
use parlov_analysis::{Analyzer, SampleDecision};

let analyzer = ExistenceAnalyzer;

// feed it a growing DifferentialSet
match analyzer.evaluate(&diff_set) {
    SampleDecision::Complete(result) => {
        println!("{:?} — {:?}", result.verdict, result.severity);
        println!("signals: {:?}", result.signals);
    }
    SampleDecision::NeedMore => {
        // collect another baseline + probe exchange and call again
    }
}

signal extractors

The signals module provides typed signal extraction from differential data:

status_code — extracts status code differentials between baseline and probe sides.
header — extracts header presence/absence/value differences (ETag, Last-Modified, Content-Range, WWW-Authenticate, Allow, Accept-Ranges).
metadata — extracts response metadata signals (Content-Range total size, ETag values).
body — extracts response body content differentials. Detects existence leakage even when status codes are identical (e.g. different error message text, different JSON error schemas).

All extractors run unconditionally on every DifferentialSet — the tool detects unexpected leakage, not just expected signals.

scoring pipeline

Confidence is computed from weighted signal scoring with diminishing returns within signal families:

Signal families (Range, CacheValidator, Auth, Precondition, Negotiation, ErrorBody) prevent double-counting correlated signals from the same RFC mechanism.
Normative weighting per signal: Must x1.0, Should x0.9, May x0.75.
Reproducibility weighting: 3/3 stable x1.0, 2/3 stable x0.7, 1/3 stable x0.25.
Verdict thresholds: >=80 Confirmed, >=60 Likely, <60 NotPresent.
Severity: highest leak impact class among validated signals, gated by confidence floor.

existence oracle

ExistenceAnalyzer implements two detection layers:

Layer 1 (code-blind): same status on first sample → NotPresent immediately. Different status → collect up to 3 pairs and check stability.
Layer 2 (RFC-informed): classifies stable differentials against a 30-pattern table — 403/404 → Confirmed/High, 409/201 → Confirmed/High, 304/404 → Confirmed/High, etc. Each pattern carries a label, leaks description, and RFC section. Unrecognized stable differentials → Likely/Low.

adding an oracle

Implement Analyzer for a new oracle class:

pub struct TimingAnalyzer;

impl Analyzer for TimingAnalyzer {
    fn evaluate(&self, data: &DifferentialSet) -> SampleDecision {
        if data.baseline.len() < 30 {
            return SampleDecision::NeedMore;
        }
        // Mann-Whitney U test on timing_ns...
        SampleDecision::Complete(Box::new(result))
    }

    fn oracle_class(&self) -> OracleClass {
        OracleClass::Timing // once the variant exists
    }
}

The binary's adaptive loop works unchanged — it just calls evaluate() until Complete, regardless of how many samples the analyzer needs.

license

MIT OR Apache-2.0

parlov-analysis 0.5.0