Fix PR #48 review finding: Doppler horizon compounding

CHANGELOG.md

@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- **Doppler horizon compounded per returning seed** (`core/power_doppler.py`): each return doubled one shared horizon (N returns → 2^N), disabling abandonment; and the dropped-key memory (`max_seeds` keys) forgot keys before large corpora cycled back. Horizon is now `max(horizon, 2 × measured gap)`; memory is 2¹⁵ keys.
+
 - **Doppler horizon thrashed on slow corpus cycles** (`core/power_doppler.py`): a cycle longer than the fixed abandon horizon dropped every frame one tick before its seed returned. The horizon now doubles when a dropped seed comes back.
 - **Stale corpus-membership memo** (`services/fuzzer.py`): keyed on corpus length, so an in-place trim of the parent kept a "member" verdict. Hits are now re-validated by slot identity; misses are not memoized.
 

docs/DEEP_DIVE.md

@@ -85,7 +85,7 @@ For production and sensitive binaries using AFL family fuzzers is the best cours
 - **AFLGo SHM-tail distance channel**: compiled into EVERY shim-linked target since `__AFL_DISTANCE_MODE` defaulted to 1 (2026-08-24; `-D__AFL_DISTANCE_MODE=0` opts out). Inert unless directed mode uploads a distance table (`DistanceTableShm`/`__AFL_DIST_SHM_ID`): without one, sum/count stay 0 and every reader takes the Python-side path. `build_targets.sh --distance` additionally builds the trace-pc-instrumented `*_dist.so`/`*_dist_asan.so` variants, where the shim accumulates per-block distances in `__sanitizer_cov_trace_pc()` — the PC (relative to the dladdr-derived object base) probes an open-addressing table of `{key, dist}` entries (packed 12-byte layout; the 4-byte header holds the slot *capacity*, a power of two ≥ 2×entries so empty slots exist, and the builder hash-inserts at `key % capacity` with linear probing to mirror the shim's probe — uploaded by the fuzzer at startup via `DistanceTableShm`/`__AFL_DIST_SHM_ID`), accumulating sum/count into the 16-byte SHM **tail** (after the edge table: `u64 dist_sum`, `u64 dist_count`), written at reset, at process exit (subprocess runs never call reset), and per-iteration in in-process modes via `__afl_dist_flush` (direct_lite has no process boundary, so the runner flushes the tail after each `run_one`). Per-execution `avg_distance = sum/count/100` is read straight from the tail and preferred over Python-side computation; blocks without a table entry don't count (AFLGo semantics). The table's PC keys are recovered by scanning text for `call __sanitizer_cov_trace_pc` sites (modern clang emits no `__sancov_pcs` for trace-pc) mapped to valued blocks via the CFGs — `TargetDistance.pc_distance_table()`. The shim's sanitizer-coverage callbacks are hidden-visibility so a libasan LD_PRELOAD cannot interpose over them in PIE builds. `tools/gen_distance_table.py` emits the table as C or text for inspection. Without the table (count==0) everything degrades to the Python-side path. Works in subprocess, direct_lite, and persistent modes (ASAN direct_lite requires libasan preloaded at fuzzer-process start — the `use_direct_lite` gate). The periodic stats line shows live distance when directed mode is active: `dist: avg:<tail avg> min:<observed min> max:<observed max>` (or `no-data`). `build_targets.sh --distance` builds both `*_dist.so` (no-ASAN) and `*_dist_asan.so` (ASAN) variants with the cmplog shim linked in, so `--cmplog` keeps them in direct_lite mode. Startup reports `[*] Distance instrumentation: detected` when the target carries the channel (the shim's `__afl_dist_flush` or a defined `__sanitizer_cov_trace_pc`), mirroring the AFL-instrumentation check. With `--elo` in directed mode, `aflgo` joins the Elo-arbitrated seed-strategy pool — a distance-pure arm picking `P(seed) ∝ exp(-2·norm_dist)` (distinct from the generic `weighted` arm, which blends distance with speed/size/entropy).
 - **AFLGo distance-annealed schedule** (`--schedule go`, requires `--target-functions`): wires the precomputed `avg_distance` (per-seed distance to directed targets) and `_anneal_progress` (exploration/exploitation annealing variable) into `SeedScorer.score()` for mutation budget scaling. During exploration phase (`anneal_progress` ≈ 0): uniform energy. During exploitation phase (`anneal_progress` → 1): `energy *= exp(β · (1 - norm_dist))` where `β = anneal_progress * 5`, capping at 100x. Seeds near the target get exponentially more mutations as the campaign matures. Previously these metrics only influenced seed selection but not mutation intensity.
 - **Power schedules** (`--schedule base|fast|coe|rare|mopt|lin|quad|go|aflgo|entropic|doppler`): AFL++ power schedules ported to control mutation budget per seed via `SeedScorer`. Each schedule modifies a base score (100) by frequency-based factors. Honggfuzz-style novelty decay, density, fertility, freshness, and entropy factors are applied multiplicatively on top. `entropic` (libFuzzer `-entropic`) scales energy by `1 + log2(1 + rare)`, where `rare` is the larger of `rare_edge_count`/`tc_ref` already collected for RARE/honggfuzz scoring — an approximation of libFuzzer's feature-frequency Shannon entropy using signal the fuzzer already tracks.
-- **Power Doppler schedule** (`--schedule doppler`, `core/power_doppler.py`): ultrasound power Doppler on coverage. Slow time = successive mutants of one seed (32 per ensemble); pixel = edge; sample = `log2(1 + hits)` (raw SHM counts, not buckets). Wall filter: mean removal (static path), then SVD components whose participation ratio spans ≥ half the seed's edges (and ≥ 4) are dropped as clutter — an early reject moving the whole path at once ("flash"). CFAR: residual power per edge vs `σ² · χ²_{dof}(1 − 10⁻³)`, `σ²` = median residual variance (floor 10⁻³). Seed power = summed flow power / dof; energy = `log1p(p)/log1p(max p)` ∈ [0, 1], scaled to `[1, max_mult]` like `katz`. Unscored or static seeds stay 1×. Gram eigendecomposition (n×n) replaces the full SVD (~6× faster). Bounded: 64 open ensembles × 2048 edges (float32, ≤16 MiB); when full, new seeds wait instead of evicting partial frames (LRU eviction never closed a frame once >64 seeds were picked in turn), and a frame untouched for 4 frames' worth of samples is scored early (≥ 3 samples) and freed; that horizon doubles whenever a dropped seed returns (slow, not abandoned), so it converges past the corpus revisit time. Scores: 4096 (LRU), flow-edge ids in int64 arrays capped at 2²⁰ total (8 MiB). Multi-target frames are keyed per target (edge ids are per-target). SHM coverage only — without it the schedule falls back to `base` with a warning; cost ~100 µs/exec at 2k live edges (dict→array conversion dominates), zero when off. Unmeasured — see `docs/TODO.md`.
+- **Power Doppler schedule** (`--schedule doppler`, `core/power_doppler.py`): ultrasound power Doppler on coverage. Slow time = successive mutants of one seed (32 per ensemble); pixel = edge; sample = `log2(1 + hits)` (raw SHM counts, not buckets). Wall filter: mean removal (static path), then SVD components whose participation ratio spans ≥ half the seed's edges (and ≥ 4) are dropped as clutter — an early reject moving the whole path at once ("flash"). CFAR: residual power per edge vs `σ² · χ²_{dof}(1 − 10⁻³)`, `σ²` = median residual variance (floor 10⁻³). Seed power = summed flow power / dof; energy = `log1p(p)/log1p(max p)` ∈ [0, 1], scaled to `[1, max_mult]` like `katz`. Unscored or static seeds stay 1×. Gram eigendecomposition (n×n) replaces the full SVD (~6× faster). Bounded: 64 open ensembles × 2048 edges (float32, ≤16 MiB); when full, new seeds wait instead of evicting partial frames (LRU eviction never closed a frame once >64 seeds were picked in turn), and a frame untouched for 4 frames' worth of samples is scored early (≥ 3 samples) and freed; when a dropped seed returns (slow, not abandoned) the horizon rises to 2× its measured revisit gap — tracking the slowest real gap, never compounding per return. Dropped keys remembered: 2¹⁵ (LRU, ~5 MiB). Scores: 4096 (LRU), flow-edge ids in int64 arrays capped at 2²⁰ total (8 MiB). Multi-target frames are keyed per target (edge ids are per-target). SHM coverage only — without it the schedule falls back to `base` with a warning; cost ~100 µs/exec at 2k live edges (dict→array conversion dominates), zero when off. Unmeasured — see `docs/TODO.md`.
 - **Favored set / cull_queue** (`core/schedules.py`, `services/fuzzer.py`): AFL-style `top_rated` minimal-set-cover selection. For each edge, the cheapest seed covering it is selected, then a greedy cover builds the favored set. FAST and COE schedules apply energy bonuses to favored seeds (`_fast_factor`, `_coe_factor`, `coe_skip`). `_cull_queue()` runs periodically during fuzzing and updates `self._favored`; the score call site passes `favored=(seed_key in self._favored)` so the scheduler actually uses it.
 - **Bayesian seed quality** (`--bayesian`): `BayesianSeedQuality` (`core/seed_quality.py`) maintains a Beta-Bernoulli posterior per seed over `P(outcome = new_coverage)`. Thompson sampling naturally balances explore/exploit without a manual temperature knob — unexplored seeds have high posterior variance and get sampled. The `record_outcome()` feedback loop is now wired in `fuzz_one()` (was previously a dead code path with all posteriors stuck at Beta(1,1)). State is persisted to `seed_quality.json` and restored on resume.
 

src/fuzzer_tool/core/power_doppler.py

@@ -41,8 +41,13 @@
 # Flow-edge ids kept across all scores: 8 B each bounds them at 8 MiB.
 DEFAULT_MAX_FLOW_IDS = 1 << 20
 # Open frame untouched for this many full frames of samples is abandoned
-# (initial horizon; doubles whenever a dropped seed comes back).
+# (initial horizon; widened to REVISIT_MARGIN x any gap a dropped seed proves).
 STALE_FRAMES = 4
+# Horizon headroom over the slowest observed revisit gap.
+REVISIT_MARGIN = 2
+# Dropped keys remembered (key -> last-touch tick); must outlast a corpus
+# cycle's worth of drops. ~150 B each bounds it near 5 MiB.
+DEFAULT_MAX_DROPPED = 1 << 15
 # CFAR false-alarm probability per edge.
 FALSE_ALARM = 1e-3
 # A component spread over at least this share of the seed's edges is a flash.
@@ -210,6 +215,7 @@ class PowerDoppler:
         max_edges: Edge columns per ensemble; extra edges are dropped.
         max_scores: Closed-ensemble scores kept (LRU).
         max_flow_ids: Flow-edge ids kept across all scores (LRU).
+        max_dropped: Dropped-frame keys remembered (LRU) to measure revisit gaps.
     """
 
     def __init__(
@@ -219,6 +225,7 @@ def __init__(
         max_edges: int = DEFAULT_MAX_EDGES,
         max_scores: int = DEFAULT_MAX_SCORES,
         max_flow_ids: int = DEFAULT_MAX_FLOW_IDS,
+        max_dropped: int = DEFAULT_MAX_DROPPED,
     ) -> None:
         if ensemble < _MIN_ENSEMBLE:
             raise ValueError(f"ensemble must be >= {_MIN_ENSEMBLE}, got {ensemble}")
@@ -230,14 +237,16 @@ def __init__(
             raise ValueError(f"max_scores must be >= 1, got {max_scores}")
         if max_flow_ids < 0:
             raise ValueError(f"max_flow_ids must be >= 0, got {max_flow_ids}")
+        if max_dropped < 1:
+            raise ValueError(f"max_dropped must be >= 1, got {max_dropped}")
         self._ensemble = ensemble
         self._max_seeds = max_seeds
         self._max_edges = max_edges
         self._max_scores = max_scores
         self._max_flow_ids = max_flow_ids
         self._stale_after = max_seeds * ensemble * STALE_FRAMES
-        # Keys whose frames were dropped as abandoned; bounded like _open.
-        self._dropped_keys: LRUCache = LRUCache(max_seeds)
+        # Dropped-as-abandoned key -> its frame's last-touch tick.
+        self._dropped_keys: LRUCache = LRUCache(max_dropped)
         # Insertion order is recency order; capacity is enforced by hand
         # (_admit, _trim) so evicted frames can be scored / ids uncounted.
         self._open: dict[str, _Ensemble] = {}
@@ -288,13 +297,16 @@ def _revisit(self, seed_key: str) -> None:
 
         A fixed horizon thrashes once the corpus cycle outlasts it, e.g. 13
         seeds in turn vs a 12-tick horizon: every frame is dropped one tick
-        before its seed returns. Doubling converges past the revisit time.
+        before its seed returns. The horizon rises to a margin over the
+        measured gap, never per return: 64 returns of a 200-tick cycle give
+        400, where doubling per key gave 2^64.
         """
-        if seed_key not in self._dropped_keys:
+        touched = self._dropped_keys.pop(seed_key, None)
+        if touched is None:
             return
 
-        del self._dropped_keys[seed_key]
-        self._stale_after *= 2
+        gap = self._ticks - touched
+        self._stale_after = max(self._stale_after, REVISIT_MARGIN * gap)
 
     def _admit(self) -> _Ensemble | None:
         """Fresh frame if a slot is free or the oldest frame is abandoned.
@@ -311,7 +323,7 @@ def _admit(self) -> _Ensemble | None:
 
             # Abandoned: score what it has rather than throw it away.
             del self._open[key]
-            self._dropped_keys[key] = True
+            self._dropped_keys[key] = old.touched
             if old.n >= _MIN_ENSEMBLE:
                 self._close(key, old)
         return _Ensemble(self._ensemble, self._max_edges)

tests/test_power_doppler.py

@@ -214,10 +214,24 @@ def test_regression_slow_cycle_beyond_horizon_still_scores(self):
 
         assert pd.stats()["ensembles"] > 0
 
+    def test_regression_many_returns_do_not_compound_horizon(self):
+        # Falsification (PR #48 review): every returning dropped key doubled
+        # one shared horizon, so N returns inflated it by 2^N and abandonment
+        # never fired again. It must track the revisit gap, not the count.
+        cap, ens, n_seeds = 4, 3, 200
+        pd = PowerDoppler(ensemble=ens, max_seeds=cap)
+        ids = list(range(PATH))
+        for _ in range(ens * 6):
+            for k in range(n_seeds):
+                _feed(pd, f"s{k}", _static(n=1), ids)
+
+        assert pd.stats()["stale_after"] <= 4 * n_seeds
+        assert pd.stats()["ensembles"] > 0
+
     def test_adversarial_abandoned_keys_do_not_grow_horizon(self):
         # Keys that never return keep the horizon; the dropped-key memory stays bounded.
         cap = 2
-        pd = PowerDoppler(ensemble=N, max_seeds=cap)
+        pd = PowerDoppler(ensemble=N, max_seeds=cap, max_dropped=cap)
         horizon = pd.stats()["stale_after"]
         ids = list(range(PATH))
         for k in range(cap * 8):
@@ -269,6 +283,8 @@ def test_bad_parameters_rejected(self):
             PowerDoppler(max_edges=0)
         with pytest.raises(ValueError):
             PowerDoppler(max_flow_ids=-1)
+        with pytest.raises(ValueError):
+            PowerDoppler(max_dropped=0)
 
 
 class TestDopplerSchedule: